59 results about "Unlabelled data" patented technology

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.). This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model and an LLM to automatically determine the relevant classification label(s) for an unlabelled data item.

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.), and applying data management policies based on the classifications. This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model to automatically determine the relevant classification label(s) for an unlabelled data item, which is then used to select and apply the relevant data management policy(ies).

The method is oriented to actual scenes of rolling bearings with scarce marks in cross-equipment and cross-working conditions, and solves the problems of instable precision and negative migration caused by domain migration under small sample and semi-supervised conditions. The invention provides a cross-domain transfer learning method of domain sensing data-meta initialization-teacher and student semi-supervision-curriculum type alignment-physical consistency-online updating. According to the method, order resampling, event anchoring slicing and robust scaling are matched with a self-supervision health index and order-preserving calibration to generate a soft / interval weak label; unlabeled is absorbed through consistency learning and uncertainty gating, and domain invariant representation is obtained according to low / middle / high level step-by-step alignment; negative migration is inhibited by combining monotone / integral consistency of the non-negative degradation rate with time deformation and spectrum keeping consistency, and rapid adaptation is realized by matching meta-learning and online small-step fine tuning; the resulting generic characterization and reusable initialization can be used for health grading, phase identification, and life-related estimation.

The invention discloses an image restoration method, system and device, a medium and a product, and belongs to the field of power grids, and the method comprises the steps: obtaining a to-be-restored image of a power scene under an extreme weather condition, inputting the to-be-restored image to an image restoration model, and obtaining a target image, the image restoration model is obtained by guiding an initial restoration model under a semi-supervised framework through an evaluation model to carry out iterative training, in each iteration, the label-free data is input into the first restoration model to obtain a restoration result, the restoration result is input into the evaluation model to carry out quality evaluation to obtain an evaluation signal, and the evaluation signal is sent to the semi-supervised framework; dynamically adjusting model parameters of a current second repair model based on the evaluation signal to guide the current second repair model to learn key features of the power equipment under extreme weather conditions, the evaluation model being obtained by performing all-parameter supervision fine tuning training on a multi-modal large language model based on power scene data; therefore, the generalization ability and the repairing effect of the repairing model can be improved by implementing the method and the device.

The application discloses a kind of semi-supervised identity recognition method and system based on millimeter wave radar, method includes: obtaining the point cloud data and range-velocity diagram data of millimeter wave radar;Point cloud data and range-velocity diagram data are preprocessed;The point cloud identity recognition model and range-velocity diagram identity recognition model designed are collaboratively trained using the point cloud data and range-velocity diagram data after preprocessing, the prediction probability of point cloud identity recognition model and the prediction probability of range-velocity diagram identity recognition model are obtained;The prediction probability of point cloud identity recognition model and the prediction probability of range-velocity diagram identity recognition model are fused.The application is trained by the method of semi-supervised collaboration to point cloud identity recognition model and range-velocity diagram identity recognition model, each other guide uses unlabelled data, improves model performance;Meanwhile, the prediction of two kinds of identity recognition models is fused, and higher identity recognition accuracy is realized.

The application discloses an uncertainty knowledge graph reasoning method based on semi-supervised confidence distribution learning, comprising the following steps: converting the triple confidence in the uncertainty knowledge graph training data into a confidence distribution; simultaneously learning the embedding of the uncertainty knowledge graph on the labeled data and the pseudo-labeled data generated by a pseudo-labeled data generator by using a relation learner based on confidence distribution learning; generating high-quality pseudo-confidence distribution labels for unlabeled data by using the pseudo-labeled data generator; iteratively training the relation learner based on confidence distribution learning and the pseudo-labeled data generator by using meta-self-training until the two converge; and inputting the data to be completed into the trained relation learner based on confidence distribution learning to perform reasoning, thereby achieving the completion of the uncertainty knowledge graph. The application can capture the supervision information of a small number of confidence or unseen confidence in the labeled data and is suitable for the scene where the triple confidence distribution of the uncertainty knowledge graph is unbalanced.

The invention relates to the technical field of computers, and discloses an equipment fault diagnosis method and device, a model training method, equipment and a medium. Firstly, target waveform data of to-be-detected equipment is acquired; next, feature extraction is carried out on the target waveform data through the equipment fault diagnosis model, and time domain features and frequency domain features are obtained; wherein the equipment fault diagnosis model comprises a semi-supervised learning module. The parameters of the equipment fault diagnosis model are updated through the semi-supervised learning module by using the unlabeled data and the labeled data, the dynamic change of the equipment state is adapted, the dependence on data labeling is effectively reduced, the training cost is reduced, and the generalization performance of the model is improved; and the equipment fault diagnosis model performs equipment fault diagnosis based on the time domain feature, the frequency domain feature, the time domain threshold range and the frequency domain threshold range to obtain an equipment diagnosis result. By realizing automatic and intelligent fault diagnosis, the accuracy and efficiency of a diagnosis result are improved.

A cross-modality knowledge transfer system is provided for adapting one or more source model networks to one or more target model networks. The system is configured to perform steps of providing the TI paired datasets through the source feature encoders of the one or more source model networks, extracting TI source features and TI source moments from the TI paired data by the BN layers of the one or more source model networks, providing the TI paired datasets and the unlabeled TR datasets through the one or more target model networks to extract TI target features and TR target moments, training jointly all the feature encoders of the one or more target model networks by matching the extracted TI target features and TR target moments with the TI source features and TI source moments along with mixing weights, and forming a final target model network by combining the trained one or more target model networks.

In one embodiment, the present disclosure provides a computer-implemented method comprising: receiving a first labeled training dataset comprising a first plurality of images each associated with a set of labels; programmatically training a machine learning neural Teacher model on the first labeled training dataset; programmatically applying a machine learning model trained for NLP to an unlabeled dataset comprising a digital electronic representation of natural language text summaries of a second plurality of images, thereby generating a second labeled training dataset comprising the second plurality of images; programmatically generating soft pseudo-labels using the machine learning neural Teacher model; programmatically generating derived labels using the soft pseudo-labels; training one or more machine learning neural Student models using the derived labels; receiving a target image; applying an ensemble of the one or more Student models to output one or more classifications of the target image.

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.), and applying data management policies based on the classifications. This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model to automatically determine the relevant classification label(s) for an unlabelled data item, which is then used to select and apply the relevant data management policy(ies).

The invention discloses an optic disc and optic cup segmentation method based on semi-supervised learning, belongs to the field of image processing and artificial intelligence, and aims to solve the problems of instability of an optic disc and optic cup segmentation model caused by few labeled eye fundus images and poor precision caused by noise pollution. In the first stage, a double-branch improved DeepLab v3 + model is constructed to complete optic disc segmentation, after full supervision and anti-noise training are carried out by utilizing an annotated fundus image, the double-branch model is used for processing an unannotated fundus image, the anti-noise capability of the model is evaluated by counting the consistency of pseudo-label pixels before and after noise, and the model with strong anti-noise performance is used for supervising the training of the other model; in the second stage, a SegFormer model is constructed and trained to complete optic cup segmentation; and finally connecting the two models in series to realize two-stage segmentation of the optic disc and the optic cup. According to the method, unlabeled data is fully utilized for semi-supervised training, the noise immunity and segmentation precision of the model are improved, and technical support is provided for glaucoma screening work.

The application discloses a kind of training method, device, electronic equipment and storage medium of image segmentation model.The method comprises: obtaining multiple groups of training sample data, and training sample data includes sample image;In the iterative training process of image segmentation model, sample image is input to the encoder in image segmentation model, and the encoding feature image is obtained, the encoding feature image is input to the decoder in image segmentation model, and the first reconstruction image is obtained, the encoding feature image is input to the variational autoencoder in image segmentation model, and the second reconstruction image is obtained;Determine segmentation model loss based on the first reconstruction image, the second reconstruction image and sample image, adjust the parameter of current image segmentation model based on segmentation model loss, and obtain target image segmentation model.The above technical solution realizes self-supervised learning by increasing variational autoencoder, realizes unlabelled data training, reduces dependence on labeled data, and reduces data labeling cost.

The invention discloses a gait identity recognition method based on a generative self-supervised visual pre-training model BEiT and DAS technology. The method comprises the following steps: firstly, carrying out scene construction and data preparation, collecting label-free data and label data, extracting data signals for signal preprocessing, and constructing a time frequency characteristic data set; constructing a pre-training image reconstruction network of a self-supervised visual pre-training model BEiT, performing feature extraction on an input image, constructing a deep learning network based on the self-supervised visual pre-training model BEiT as a feature extractor, and training the deep learning network by using unlabeled spatio-temporal data in an experimental scene to obtain weight parameters; and constructing a downstream classification task network, performing classification learning of downstream tasks by using a small amount of data with labels to obtain a new weight, and completing identification and classification of non-label data by using the obtained new weight. According to the method, the demand quantity of the tagged data is reduced, and the accuracy is ensured.

The application discloses a wall heat flow prediction method considering material ablation response under a semi-supervised learning framework, and relates to the field of aerospace. The method comprises the following steps: constructing a data set; building a neural network model and introducing an ablation physical information loss term into a loss function of the neural network model; pre-training the neural network model; predicting unlabelled data through the pre-trained neural network model to generate pseudo labels; mixing labelled data and unlabelled data to semi-supervise and train the pre-trained neural network model to obtain a final neural network model; and predicting wall heat flow of material ablation response through the final neural network model. The method reduces the demand of the model on the labelled data set, enhances the model generalization ability under the condition of small sample training, increases the physical interpretability of the network model, and improves the wall heat flow prediction precision.

The invention relates to a computational lithography modeling and hot spot detection method and device based on self-supervised learning, and the method comprises the steps: constructing a neural network model which comprises a layout feature encoder, a geometric reconstruction branch and a lithography space image prediction branch, and enabling a differentiable optical simulation layer to be built in the lithography space image prediction branch so as to integrate physical prior; after an input layout is masked based on label-free mask plate graph data, the model is driven to execute pre-training of geometric reconstruction and photoetching space image prediction at the same time, an original unmasked layout is processed through the differentiable optical simulation layer to generate a pseudo-true value, and a supervision signal is provided for photoetching space image prediction; and on the basis of the pre-training model, performing fine tuning by using a small amount of labeled data to realize hotspot detection. The differentiable optical simulation layer is integrated into the self-supervised learning framework, so that the model can learn the optical law from massive label-free data, the dependence on labeled data is reduced, and the hotspot detection capability and accuracy of unknown graphs are improved.

The invention discloses a rotating machine fault classification method based on semi-supervised transfer learning, and relates to the field of fault diagnosis, and the method comprises the following steps: S1, collecting a vibration signal containing transient noise and a small number of fault labels; s2, positioning and removing transient noise by using a first-order Markov model, and dividing a 80% training set and a 20% test set; s3, building a semi-supervised transfer learning model; s4, training a semi-supervised transfer learning model; s5, the model is tested, and it needs to be ensured that the accuracy rate is larger than or equal to 95% under the label rate of 5 And S6, performing online application. According to the method, semi-supervised and transfer learning are fused, the non-label data value is effectively mined, transient noise is suppressed, the blank of low-label and high-interference working condition diagnosis is filled, a data driving method is promoted to move from a laboratory to an industrial complex scene, and key technical support is provided for field improvement of fault detection accuracy and reliability and predictive maintenance upgrading.

A system and method for classifying products. A processor generates a first instance and a second instance of a first classifier and trains the instances based on an input dataset. A second classifier is trained based on an input, wherein the second classifier is configured to learn a representation of a latent space associated with the input. A first supplemental dataset is generated in the latent space, wherein the first supplemental dataset is an unlabeled dataset. A first prediction for labeling the first supplemental dataset is generated based on the first instance of the first classifier; and a second prediction for labeling the first supplemental dataset is generated based on the second instance of the first classifier. A labeled annotation of the first supplemental dataset is generated based on the first prediction and the second prediction. A third classifier is trained based on at least the input dataset and the annotated first supplemental dataset.

The invention discloses an out-of-distribution data learning method and system for agent navigation. The method aims at improving the capability of utilizing out-of-distribution data in a navigation task of a machine learning model. By optimizing the navigation strategy, a large amount of distributed external navigation data which is easy to obtain but does not reach the target position can be effectively utilized. According to the main technical scheme, the method comprises the steps that a navigation data set containing expert demonstration and out-of-distribution demonstration is collected; training a reverse environment model and a reverse navigation strategy; reverse data enhancement and data resampling are executed through self-paced learning; and finally, combining off-line behavior cloning and reinforcement learning, adopting a behavior cloning method for marked data containing expert behaviors, implementing approximate dynamic planning on unmarked data, and establishing an optimization target and an optimization navigation strategy in an out-of-distribution state. The method has the characteristics of easiness in data acquisition, easiness in algorithm implementation and the like, and based on few expert demonstration, the decision-making ability of the intelligent agent in a complex navigation environment is steadily improved.

One aspect of the invention provides a computer-based method for providing corrective feedback about exercise form, the method comprising; recording a user performing a specific exercise: evaluating, by the computer, with machine learning, computer vision, or deep learning models that have been previously trained in order to evaluate the form of a user by training on labelled and or unlabeled datasets that consist of: both correct and incorrect exercise form for the different types of exercises being evaluated; identifying the user throughout the video, the exercise type, each repetition of the exercise, the user's errors in form; and then generating, by the computer, corrective feedback for the user on how to improve exercise form for subsequent repetitions; and communicating, via an output device, the corrective feedback to the user.

A computer-implemented method providing a neural network for identifying radio technologies employed in an environment. The neural network includes an autoencoder having an encoder, and a classifier. The method has the steps of sensing a radio spectrum of the environment thereby obtaining a set of data samples, labelling a subset of the data samples by a respective radio technology thereby obtaining labelled data samples, training the autoencoder in an unsupervised way by unlabelled data samples, training the classifier in a supervised way by the labelled data samples, and providing the neural network by coupling the output of an encoder network of the autoencoder to an input of the classifier.

A computer-implemented method of neural architecture search. The method includes: training a supermodel by sampling an architecture and training the supermodel with the sampled architecture on labeled training data and updating weights of the supermodel by gradients with respect to the sampled models; determining Pareto-optimal submodels of the supermodel based on at least two performance metrics by iteratively carrying out the following steps: computing outputs of a reference model for the unlabeled data, wherein the reference model a largest submodel of the supermodel; sampling a plurality of submodels from the supermodel; computing by the submodels their outputs of the unlabeled data; computing a difference between the outputs of the reference model and the submodels; employing an optimization algorithm to iteratively sample and evaluate submodels based on a plurality of objectives.

A processor-implemented method for adapting an artificial neural network (ANN) at a test time includes receiving a test data set by a first ANN model and a second ANN model. The test data set includes unlabeled data samples. The first ANN model is pre-trained using a training data set and the test data set. The first ANN model generates a first estimated tag for the test data set. The second ANN model generates a second estimated tag for the test data set. Samples of the test data set are selected based on a confidence difference between the first estimated tag and the second estimated tag. The second ANN model is retrained based on the selected samples.

The application discloses a kind of semi-supervised medical volume segmentation methods based on cyclic variation pseudo label, the algorithm used in this method gives a brand-new semi-supervised medical volume segmentation architecture: the segmentation threshold is regarded as latent variable, constructs deep bayesian network to carry out variation inference, excavates potential information in unlabelled data to obtain more reliable variation pseudo label;While using two segmentation networks of parallel isomerism provides model level disturbance to construct additional correction signal, uses variation pseudo label generated by each other in one iteration to carry out cyclic supervision, corrects respective cognitive bias, guides two networks to make consistent low entropy prediction, the method of the application only needs to use a small amount of labeled data, can reach the performance comparable to full supervision learning.

The invention belongs to the technical field of hydrogen safety, and discloses a hydrogen leakage positioning method, device, equipment and medium, the method comprises the following steps: carrying out Fourier transform on unlabeled multi-source hydrogen concentration sensor sequence data to obtain frequency domain characteristics, and respectively enhancing time domain data and frequency domain data; time-frequency domain characterization is extracted through a Transform double encoder, and comparison loss is constructed; mapping to a joint space through a projection network, and obtaining a final time-frequency representation through triple comparison loss optimization; and inputting BP neural network training to realize positioning. According to the method, high generalization features are extracted from label-free data, dependence on labeled data is reduced, the weak signal detection capability is improved by fusing time-frequency domain features, and a leakage source can be quickly positioned.

Aspects concern a server for facilitating constructing a brand platform, comprising: a memory; and a processor configured to: obtain labelled data and unlabelled data for each item, wherein the labelled data includes an input text for the item and a brand label corresponding to the input text; obtain information about a plurality of brands from a master brand table; for the labelled data, predict a relevant brand which is relevant to the input text from the plurality of brands, and predict a relationship between the brand label and the relevant brand; for the unlabelled data, predict whether the unlabelled data includes new brand information about the relevant brand, based on the information about the plurality of brands; generate updated information about the relevant brand, based on the predicted relationship and the new brand information; and train the master brand table based on the updated information about the relevant brand.

The application discloses an active anomaly detection method of data independent subset driving multi-expert hybrid training. Through active learning feedback in each round, samples are divided into two parts according to the setting of a threshold value according to the ranking of an anomaly score, and multiple sample subsets are divided based on the similarity and diversity of features. A plurality of weak expert groups are independently trained on each subset in the form of an expert hybrid network, reasoning is performed by means of integration, and the most abnormal samples are cross-selected on each expert network, so that the interference caused by the single-class setting of unlabelled data to the model is reduced. The application specifically applies a multi-task expert hybrid network model to single-task anomaly detection, intelligently learns the weight of integration and combination, strengthens the connection between data and expert networks, improves the detection performance, and provides a new idea for semi-supervised anomaly detection.

The present disclosure provides an anomaly detection method, device, electronic equipment, storage medium and program product, which comprises: determining unlabelled data of a task to be detected, performing local feature extraction on the unlabelled data to obtain at least one unlabelled data feature; performing feature matching on the at least one unlabelled data feature through a pre-constructed feature matching model to obtain at least one near-neighbor feature segment corresponding to the at least one unlabelled data feature; performing distance calculation on the at least one unlabelled data feature and the at least one near-neighbor feature segment to obtain at least one anomaly score corresponding to the at least one unlabelled data feature; and performing detection based on the at least one anomaly score to obtain an anomaly region. The present disclosure can improve the detection performance on dynamic data and unknown anomalies, and is closer to actual application.

A radar target detection method and device based on cross-view cross-modal contrast learning and a medium are provided.The method constructs a self-supervised learning framework for radar target detection based on a distance-azimuth-Doppler (RAD) tensor, captures the relationship between different views of the RAD cube through cross-view contrast learning, enables the model to obtain spatial distribution knowledge from multiple views, and captures the relationship between the RAD and a visual modality through cross-modal contrast learning, enabling the model to obtain semantic knowledge from the visual modality.The present application combines cross-view and cross-modal contrast learning, enhances the feature representation capability of the radar, allows the RAD model to learn transferable feature representations from unlabelled data, and significantly improves the accuracy of radar target detection downstream tasks in the case of limited labelled data.

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.), and applying data management policies based on the classifications. This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model to automatically determine the relevant classification label(s) for an unlabelled data item, which is then used to select and apply the relevant data management policy(ies).

The invention discloses a carrying equipment bearing fault diagnosis method and device based on self-supervised learning, and the method comprises the steps: collecting vibration signals during the operation of a carrying equipment bearing, and enabling the vibration signals to comprise time domain data signals of different fault states of the bearing; preprocessing the vibration signals of the bearing to obtain final time domain and frequency domain representations of the vibration signals under the training set, the fine tuning set and the test set; constructing a TFDDCF model to pre-train the time domain representation and the frequency domain representation, and introducing a TF comparison loss function and a TF fusion loss function into the TFDDCF model to pre-train and supervise the domain representation and the frequency domain representation; and constructing a target diagnosis model MTFDDCF based on the TFDDCF model to carry out learning training and fine tuning, inputting the test set into the fine-tuned target diagnosis model MTFDDCF to extract features in the test signal, and classifying different types of features. According to the method, noise interference can be effectively reduced, the capacity of extracting unlabeled data feature information is high, the structure is simple, the accuracy is high, and intelligent carrying equipment bearing fault diagnosis can be achieved.