136 results about "Label free" patented technology

The invention discloses a multi-agent dynamic task allocation and collaborative path-finding system based on label-free distributed deep reinforcement learning. The system comprises the following steps: step 1, receiving state information and environment perception data of each agent in a multi-agent system based on distributed deep reinforcement learning; step 2, extracting feature representations of the environmental perception data and the intelligent agent state information, and performing multi-source heterogeneous information fusion through an attention mechanism to obtain state-task matching features; 3, transmitting the state-task matching characteristics to a multi-agent network in real time, realizing task allocation negotiation among agents by adopting a hierarchical scheduling and state exchange mechanism based on task priorities, dynamically detecting newly added task types, and performing incremental learning; the online updating iteration of the model is realized to assist the multi-agent optimization task allocation strategy and the path planning action; compared with the prior art, the method has the advantages that by applying the distributed deep reinforcement learning technology and a state exchange mechanism between intelligent agents, the system can quickly adapt to environment changes and task dynamics, and the resource utilization rate and the task completion efficiency are improved.

The invention provides a power station photovoltaic module defect detection method and device based on unsupervised learning. The method comprises the steps that firstly, a thermal imaging image to be detected and a visible light image in a target area provided with a photovoltaic module are collected; cutting a thermal imaging photovoltaic module image from the thermal imaging image, and cutting a visible light photovoltaic module image from the visible light image; afterwards, a teacher network model and a visible light photovoltaic student network model in the photovoltaic defect detection model are used for processing the visible light photovoltaic module image, and position information of the visible light abnormal photovoltaic module is determined and returned; and the teacher network model and the thermal imaging photovoltaic student network model in the photovoltaic defect detection model are used to process the thermal imaging photovoltaic module image, and the position information of the thermal imaging abnormal photovoltaic module is determined and returned. Wherein the photovoltaic defect detection model is obtained by training in a label-free supervision mode.

The invention discloses an encrypted traffic detection method based on LLM enhanced semi-supervised learning. The method comprises the following steps: firstly, training a traffic detection model for detecting encrypted traffic under an LESS framework; and detecting the encrypted traffic by using the trained traffic detection model. Components of the LESS framework include a Prompt orchestrator, an NSSN, and an LLM optimizer. The components work cooperatively, a detection model is trained by using a labeled training set and a label-free training set, and each piece of data in a data set is formed by arranging length values of data packets in a network flow in sequence; the Prompt composer guides the LLM to generate label-free enhanced data in different scenes; the NSSN automatically sets a pseudo label for the enhanced label-free traffic data, and learns traffic detection knowledge accordingly; under the guidance of the SSL loss, the LLM optimizer adjusts the enhancement strategy of the LLM to align the semi-supervised training requirements of the detection model. The training steps of the detection model include LLM pre-fine tuning; marking the LLM enhanced data; and continuous demand alignment can be realized.

The invention provides a graphical interface agent training method and device and a storage medium, and the method comprises the steps: deducing an initial state and a transition state corresponding to an action in a first unlabeled graphical interface interaction track based on a dynamic model, and enabling the initial state and the transition state to serve as a supervision signal of the first unlabeled graphical interface interaction track; on the basis of an inverse dynamic model, inferring an action corresponding to state transition in the second label-free graphical interface interaction track, and taking the action as a supervision signal of the second label-free graphical interface interaction track; and training the graphical interface agent based on the first untagged graphical interface interaction track with the supervision signal and the second untagged graphical interface interaction track with the supervision signal. Therefore, the supervision signal is automatically extracted from the unlabeled data, rich information contained in the unlabeled data is fully utilized, the economic and time cost of data labeling is reduced, and the performance of the GUI intelligent agent on key technical indexes such as multi-step task planning and interface understanding is improved.

The invention discloses an unsupervised industrial defect detection method, system and device based on a multi-mode expert system. The unsupervised industrial defect detection method comprises the following steps: acquiring a label-free industrial defect image data set and a text cue word; inputting into a multi-modal expert system to obtain a corresponding prediction result; screening out a preliminary labeling result; filtering the preliminary labeling result based on a prior constraint to obtain a fine label; inputting the industrial defect image data set and the fine label into a self-supervised model for training, and outputting a pseudo label after training is completed; performing double filtering on the pseudo labels by adopting a dynamic threshold value and a prior constraint; inputting the industrial defect image data set and the pseudo label after the current training double filtering into the self-supervised model after the last training for re-training, outputting the pseudo label, and returning to execute the double filtering until the trained self-supervised model is obtained; and predicting an industrial defect image to be detected by adopting the trained self-supervised model to obtain a corresponding industrial defect detection result. And the detection accuracy and efficiency are improved.

The invention provides a single base station error suppression and positioning method based on unsupervised learning, which automatically models to reach error distribution of a phase difference without label data through a deep learning network, and does not need manual labeling. The system extracts signal features, and non-line-of-sight propagation errors are effectively identified and corrected by using unsupervised learning. Compared with a traditional multi-base-station method, high-precision positioning can be achieved in a complex indoor environment only through a single base station. The method combines the feature extraction capability of deep learning and the distribution modeling of unsupervised learning, significantly reduces the deployment cost, improves the positioning precision and robustness, is especially suitable for scenes with significant dynamic and multipath effects, provides an innovative solution for ultra-wideband positioning, and has a wide application prospect.

The invention discloses a railway foreign matter invasion identification method, system and device and a medium, and the method comprises the following steps: obtaining multi-modal data, the multi-modal data comprising a video and / or an original railway image; preprocessing the video and / or the original railway image to obtain a preprocessed image; performing image enhancement processing on the preprocessed image to obtain a target enhanced image; performing image segmentation prediction on the target enhanced image to obtain a segmentation prediction result; performing post-processing on the segmentation prediction result to obtain a foreign matter recognition result; wherein the post-processing refers to reserving an identifier corresponding to the abnormal target in the rail-mounted area. According to the method, rapid and effective foreign matter recognition processing can be carried out on mass label-free data, the foreign matter invasion target in the railway track area can be effectively detected in real time, and the situations of false detection and missing detection of an algorithm are reduced.

The invention discloses an underwater in-situ fish label-free sample detection method and equipment. The method comprises the following steps: acquiring an in-situ image shot by a monocular camera of an underwater observation platform, and constructing an image-text structured knowledge base; extracting a training and verification data set of the detection model from the knowledge base, and dividing the data set into a closed set category and an open world category; receiving a natural language text instruction which is input by a user and is used for describing the to-be-detected data set, and generating a text feature vector of a target category; a targeting auxiliary model is obtained through vision-text semantic space training, dynamic interactive fusion is carried out on the visual features of the in-situ image and the text features of the category, and end-to-end joint optimization is carried out on the target model; and performing label-free detection on fishes appearing in the underwater in-situ image by using the optimized target model. According to the method, new species which are not seen during training can be effectively identified, cross-domain reasoning can be implemented on a label-free observation sample, and the retraining and maintenance cost is reduced.

The invention provides a semi-supervised target detection model training method and system based on a hybrid learning framework. The method comprises the following steps: acquiring a teacher model and a student model; obtaining a training set; performing strong enhancement operation on any labeled data, and inputting the labeled data into the student model to obtain a labeled detection result; calculating supervised loss; performing weak enhancement operation and strong enhancement operation on the label-free data, inputting the label-free data into a teacher model for detection to generate a pseudo label, and inputting the label-free data into a student model for detection to obtain a label-free detection result; extracting a pseudo negative label; calculating pseudo label loss and pseudo negative label loss to form unsupervised loss; and iteratively updating parameters of the student model and the teacher model. In the hybrid learning strategy provided by the invention, the positioning accuracy of the model on the target is enhanced by positive learning; the negative learning effectively improves the utilization rate of label-free data, reduces the propagation of noise in pseudo labels, and finally remarkably improves the training efficiency and the accuracy of model detection.

The invention discloses a quantum federal collaborative distillation method, which comprises the following steps of: coordinating and serially connecting a whole federal learning process through a federal server, selecting clients participating in training in each round through a non-put-back sub-sampling method to realize first privacy amplification of privacy, providing a public non-label data set and finishing aggregation of soft labels; the obfuscator anonymously obfuscates the received information through a shuffling method, so that the federated server cannot associate any piece of information with a sender of the federated server, and second privacy amplification of differential privacy is realized under the condition that collusion with the federated server is avoided; and a client performs quantum model training on the local data set, predicts the public label-free data set, adds noise, encrypts and uploads the public label-free data set, so that knowledge distillation is realized, and third privacy amplification of differential privacy is realized by smoothing the same cluster sample. The invention also provides a quantum federal collaborative distillation system, electronic equipment and a storage medium.

The invention discloses a graph backdoor encoder defense method and device, and a readable storage medium. The method comprises the steps that at least two label-free enhanced data sets are generated based on a preset training data set, a to-be-processed encoder is trained based on the enhanced data sets to obtain a teacher encoder, and the training data set is a downstream data set or a subset of the downstream data set; defining identical attention operators in corresponding layers of the teacher encoder and the to-be-processed encoder, wherein the attention operators are used for outputting layer attention maps of corresponding encoder layers; calculating the distribution offset of the layer attention map of the teacher encoder and the layer attention map of the encoder to be processed in the same encoder layer; and fixing the parameters of the teacher encoder, and updating the parameters of the to-be-processed encoder based on the distribution offset. Compared with the prior art, through lightweight deployment, on the premise that the model precision is not reduced, the attack success rate of backdoor attack on graph self-supervised learning is effectively reduced, and the backdoor defense capability of the system is improved.

The invention provides a railway internal data circulation sharing method based on semi-supervised federated learning, and relates to the technical field of semi-supervising, and the method comprises the steps: obtaining a plurality of initial local models from a plurality of clients; obtaining an initial global model; allocating a pseudo tag to each piece of no-tag data in the no-tag data set; obtaining a plurality of local models; distributing a corresponding weight parameter to each local model; and obtaining a data sharing model. According to the method, the model obtained through training based on the label data set is used for distributing the pseudo labels for the non-label data, so that the non-label data can participate in subsequent model training, the scale of the training data is further expanded, and the generalization ability of the model is improved; and distributing corresponding weight parameters to each local model based on the clustering distribution of the data on each client, thereby reflecting the importance of each local model in the data sharing model, so that the finally obtained data sharing model not only can consider the global generality, but also can reflect the personalities of different clients.

A label-free cell classification and screening system based on hybrid transfer learning, including a data preprocessing module for acquiring 2D light scattering video data and for digital cell filtering, is made public here; the data preprocessing module includes the label-free high-content video flow cytometry, which has the optical excitation module, the sheath flow control module, and the data acquisition and processing module; the image archiving module is used to sort and set labels for cells; in the feature extraction module, the first convolutional neural network is used to obtain image data feature vectors; in the cell classification and screening module, a support vector machine model is used to obtain the cell screening results.

The invention provides an industrial data label-free anomaly detection algorithm, which comprises the following steps: carrying out sliding window processing on obtained continuous production time sequence data, carrying out unsupervised clustering learning, and combining a clustering result as an additional feature into the continuous production time sequence data; performing redundant information processing on the obtained time series data of the K clustering labels by adopting a low-rank graph space-time network method, and finally outputting the data combining time and space characteristics by using full connection; using a recurrent neural network to further enhance the learning of time sequence features of the obtained high-dimensional spatial-temporal feature data, and outputting data distribution in the high-dimensional spatial-temporal feature data; and carrying out probability distribution statistics on the obtained data distribution, and judging whether the time sequence data in the sliding window time period is abnormal or not by combining a clustering result. According to the technical scheme, the abnormal data of the label-free industrial scene assembly line is rapidly detected by using the label-free abnormal data training learning and the low-rank map deep learning network, and the speed and accuracy of risk early warning are effectively improved.

Some embodiments can include methods and related systems to project coarse representations of natural language interactions into fine-grained representations using contrastive learning projections. Some embodiments can maximize a first set of distances between dissimilar points and anchor points in the fine-grained representations, and minimize a second set of distances between similar points and the anchor points in the fine-grained representations. Some embodiments can then propagate labels from labeled projections to unlabeled projections based on a similarity metric.

The invention discloses a semi-supervised image annotation method based on a limited label data set, and the method comprises the steps: taking a FixMatch frame as a basis, and integrating a learnable batch normalization module, a dual-scale parallel convolution module, a content and style separation dual-branch module and a dynamic residual gating module in a ResNet backbone network, the stability of feature extraction and the adaptive capacity to enhanced disturbance are improved. For a label-free sample, a multi-level pseudo-label fusion mechanism is provided, prediction distribution of weak, medium and strong enhanced views is synthesized, and high-confidence pseudo-labels are generated through confidence weighted fusion of multi-level enhanced views and comparison and screening with a category threshold. On the basis, a joint loss function composed of label supervision loss and pseudo label consistency loss is constructed, and a plurality of key control parameters in FixMatch + + are adjusted and optimized in a pre-experiment and grid search combined mode to obtain a group of optimal parameters of the model. Finally, a user inputs a label-free image into the trained FixMatch + + model, and the model can automatically generate a high-confidence pseudo label, so that the number of labeled samples in a limited labeled image set is increased, and the classification precision is improved. By implementing the method, the manual annotation cost can be reduced, and efficient and reliable support is provided for image analysis and recognition tasks.

A small sample sintering condition identification method based on flame video multi-scale attention modeling comprises the following steps: acquiring rotary kiln field flame video condition data, extracting global condition characteristics and local condition characteristics of a known label flame video condition sample, and performing parameter update learning of a model; extracting global working condition features and local working condition features of the unlabeled flame video working condition samples; calculating the similarity between the global working condition characteristics and the local working condition characteristics of the flame video abnormal working condition sample with the known label and the non-label flame video working condition sample, and calibrating a corresponding abnormal working condition false label for the non-label flame video working condition sample according to the characteristic similarity, or discarding the non-label flame video working condition sample; and adding the flame video working condition samples marked as the abnormal working condition false labels into flame video working condition samples with known labels to form a sample training sample set, and retraining the model by using the training sample set. The small sample abnormal working condition identification precision can be improved, and the calculation complexity of the model is reduced.

The invention relates to the field of three-dimensional vision, in particular to a label-free three-dimensional point cloud segmentation method based on a visual large model, which comprises the following steps: acquiring three-dimensional point cloud data, and extracting a boundary point set according to the three-dimensional point cloud data in combination with curvature and normal vector to construct a target function; solving the objective function by using an optimization algorithm, selecting an optimal projection visual angle combination, and extracting two-dimensional features of all two-dimensional projection images and three-dimensional features of three-dimensional point cloud data by using a visual large model; performing consistency matching on the two-dimensional features and the three-dimensional features through a matching algorithm to obtain a plurality of matching results; acquiring a definition weight factor and a coverage rate weight factor to calculate an importance weight; fusing the two-dimensional features according to the weight factors to obtain overall fused two-dimensional features; and inversely mapping the integrally fused two-dimensional features to a three-dimensional space to realize three-dimensional point cloud unmarked segmentation. The three-dimensional point cloud segmentation method has the effect of realizing three-dimensional point cloud automatic segmentation without labels.

The invention discloses a gesture posture estimation method based on multiple visual angles and semi-supervised learning, and belongs to the technical field of man-machine interaction, the gesture posture estimation method comprises the following steps: collecting gesture images through multiple visual angles, constructing a label-free data set, labeling a small amount of gesture posture data to form a label data set with hand key points; then randomly selecting one from a plurality of visual angles as input, forming a hidden code through an encoder constructed by a neural network, mapping the hidden code into a gesture image of another visual angle through a decoder to realize consistency constraint of different visual angles, mapping the hidden code into 2D key point coordinates through a projection converter, and finally obtaining a gesture image of the other visual angle; the method comprises the following steps of: randomly inputting a multi-view label-free sample or a small number of label samples for training under the combined action of two constraint mechanisms, and generating 3D key point coordinate estimation of a gesture posture only by inputting a gesture image into an encoder in practical application.

The invention discloses a label-free data deep learning method based on an auto-encoder structure, and the method comprises the steps: obtaining a label-free data set, carrying out the unified normalization preprocessing, and constructing a standardized input tensor; respectively extracting potential variable representation and semantic representation through a first branch network of a variational auto-encoder structure and a second branch network extracted by semantic representation; on the basis of the corresponding relation between the two branch networks, potential space alignment loss is constructed, and the potential space alignment loss, reconstruction loss and semantic comparison loss jointly form a joint loss function, so that joint optimization of the two branch networks is realized. And finally outputting the optimized label-free data feature representation for data input of various downstream tasks. According to the method, the representation quality of the label-free data is effectively improved, the feature extraction and representation capability of the deep learning model in an unsupervised scene is enhanced, and the method has a wide application prospect.

A particulate matter detection device takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides larger particles to fly through an imaging window. The flowing particles are illuminated by a pulsed laser diode, casting their inline holograms on a CMOS image sensor in a lens-free mobile imaging device. The illumination contains three short pulses with a negligible shift of the flowing particle within one pulse and triplicate holograms of the same particle are recorded at a single frame revealing different perspectives of each particle. A deep neural network classifies the particles based on the acquired holographic images. The device was tested using different types of pollen and achieved a blind classification accuracy of 92.91%. This mobile and cost-effective device weighs ˜700 g and can be used for label-free sensing and quantification of various bio-aerosols over extended periods.

The invention discloses a data matching method, apparatus and device, and a storage medium. The method comprises the steps of constructing a to-be-discriminated data set and a training data set according to a plurality of sample pairs corresponding to a plurality of to-be-matched data sets; determining a plurality of pseudo label sample pairs and a plurality of to-be-discriminated sample pairs according to the initial discriminator and the to-be-discriminated data set; performing real label labeling on the plurality of to-be-discriminated sample pairs to obtain a plurality of labeled sample pairs, and updating the training data set in combination with a plurality of pseudo label sample pairs; and performing iterative training on the initial discriminator through the updated training data set, obtaining a sample label of each label-free sample pair according to the initial discriminator after the iterative training, and further performing data matching and storage on data samples existing in the data set. By means of the mode, the labeling cost is remarkably reduced, semi-supervised cooperative training is combined, the problem of data sample imbalance is effectively solved, the value of labeling data on model training is improved, and precise matching of complex data attributes is achieved.

The present invention specifically relates to a semi-supervised stance detection method based on category-aware curriculum learning, including: detecting predicted stance labels of tweets in a stance detection model; training the model includes: pre-training the stance detection model through a set of labeled tweets; inputting a set of unlabeled tweets into the pre-trained stance detection model, outputting pseudo-labels of unlabeled tweets, and generating a set of unlabeled tweets with pseudo-labels; selecting corresponding unlabeled tweets from a set of unlabeled tweets with pseudo-labels based on curriculum learning to form a set of pseudo-labeled tweets; optimizing the pre-trained stance detection model through the set of pseudo-labeled tweets to obtain a final stance detection model. The present invention reduces the demand for labeled data for model training through semi-supervised stance detection, and at the same time pays more attention to the quality of pseudo-labels required for semi-supervised stance detection through a curriculum learning framework, and fully considers the problem of label category balance when selecting pseudo-labeled tweets.

The present invention discloses a mobile path planning method, device, robot and storage medium. The method comprises: when the robot triggers navigation resumption, determining the label type of the current label corresponding to the current position of the robot based on the collected image data; the label type includes at least one of: no label, legal label and illegal label; and planning the movement path of the robot based on the label type. By running the technical solution provided by the embodiment of the present invention, the problem of the robot passively leaving the originally planned movement path during autonomous movement, resulting in the interruption of the robot's navigation state and the inability to continue moving, which reduces the robot's movement efficiency and task execution efficiency, can be solved, thereby achieving the beneficial effect of improving the robot's movement success rate and movement efficiency.

In order to make full use of a large amount of label-free face data to cope with the actual problems of slow labeling progress, label shortage and the like in the initial stage of face data set construction, the invention provides a body constitution recognition method based on a label-free face image. Taking the facial constitution data as a target domain for feature extraction to obtain source domain features and target domain features; according to the category center feature vector of the source domain feature sample, respectively determining optimal transmission matching streams of the source domain feature sample and the category center feature vector as well as the target domain feature sample and the category center feature vector; and determining the classification probability of the source domain feature sample according to the optimal transmission matching flow, and identifying the physique according to the classification probability.

The invention discloses a model training method, an information release method and device, equipment and a medium, and belongs to the technical field of computers. The method comprises the steps that a positive sample, a negative sample and a label-free sample are obtained, the positive sample comprises object features of a put object and a positive sample label, the negative sample comprises object features of the put object and a negative sample label, and the label-free sample comprises object features of a non-put object; training a label prediction model based on the positive samples and the negative samples; predicting a pseudo label of the label-free sample through a label prediction model obtained through training; and based on the positive sample, the negative sample, the unlabeled sample and the pseudo label of the unlabeled sample, training a delivery prediction model, the delivery prediction model being used for predicting the delivery score of any object. The training process of the delivery prediction model can globally learn the features of the delivery object and the non-delivery object, so that the accuracy of the delivery prediction model is improved, and the delivery precision can be improved.

The present invention relates to a semi-supervised model generalization method that integrates label denoising, comprising the following steps: 1. Using a pseudo-label generation model to generate pseudo-labels for unlabeled source domain data; 2. Using a dual calibration generalization model to learn on the source domains of pseudo-labels and true labels, selecting clean samples for exchange based on a small-loss strategy, and updating both sides on the selected clean samples. Simultaneously, a style confusion module is inserted to improve the model's generalization capability; 3. In an intermediate domain comprising a labeled source domain and an unlabeled source domain with clean samples, a progressive intermediate domain generation module linearly mixes these two samples, which are then fed into the next cycle as new labeled source domain data for the pseudo-label generation model. This method is beneficial for improving the model's generalization capability.

The application belongs to the field of artificial intelligence model compression and edge deployment in intelligent operation and maintenance and fault diagnosis of power equipment, and discloses a transformer cooperative distillation incremental learning method and system based on time sequence consistency, which comprises the following steps: a teacher model is used to screen a no-label sample set to obtain a pseudo-label sample set, the pseudo-label sample set is combined with an original label sample set to obtain a distillation training sample set; a multi-mechanism cooperative distillation training scheme containing soft and hard label joint distillation, time sequence consistency distillation and multi-task distillation is constructed, and a student model is subjected to distillation training through the multi-mechanism cooperative distillation training scheme; and a cooperative distillation incremental learning scheme in which a cloud end continuously updates a teacher model to adapt to new multi-source monitoring data and a student model after edge distillation generates a pseudo-label to expand the distillation training sample set is constructed. The application can be used for realizing high-precision and low-cost training and continuous updating of a student model in a resource-limited terminal or an online scene.

The invention discloses a power distribution network discrete action equipment scheduling method and device and a nonvolatile storage medium. The method comprises the following steps: determining load prediction data of a power distribution network in a preset time period and active output prediction data of a distributed power supply of the power distribution network; a discrete action equipment scheduling model is called to process the load prediction data and the active power output prediction data, so that a scheduling scheme of discrete action equipment in the power distribution network is obtained, and training data of the discrete action equipment scheduling model comprises label data and label-free data; the discrete action equipment scheduling model comprises a feature extractor used for identifying data input into the discrete action equipment scheduling model; and adjusting the tap switching position of the discrete action equipment in a preset time period according to the scheduling scheme. According to the method and the device, the technical problem that the discrete action equipment in the power distribution network cannot be automatically dispatched due to the fact that the output result of a discrete action equipment dispatching model in the related technology is inaccurate is solved.

The invention provides a few-label industrial process quality prediction method based on a DCMSViT model, and relates to the technical field of industrial process monitoring, and the method comprises the steps: carrying out the preprocessing of historical data, and dividing the historical data into a labeled sample set and an unlabeled sample set; then, a dynamic collaborative regression module is utilized to generate pseudo labels for unlabeled samples based on labeled samples, and high-confidence pseudo label samples are screened out through a dynamically calculated confidence threshold, so that a labeled training set is expanded; then, the expanded labeled sample set and the remaining unlabeled sample set are input into a multi-scale visual Transform module together for training, and the multi-scale visual Transform module is used for extracting global feature vectors of the data so as to carry out quality prediction; and finally, the trained DCMSViT model is utilized to realize real-time and online prediction of the key quality variables of the industrial process. According to the method, a small amount of label data and a large amount of label-free data are effectively utilized, and the accuracy of industrial process quality prediction and the model generalization ability in a small-label scene are remarkably improved.