454 results about "Domain adaptation" patented technology

Sound, robust methods identify the most suitable, parsimonious set of tests to use with respect to prioritized, sequential anomaly detection in a collected batch of sample data. While the focus is on detecting anomalies in network traffic flows and classifying network traffic flows into application types, the methods are also applicable to other anomaly detection and classification application settings, including detecting email spam, (e.g. credit card) fraud detection, detecting imposters, unusual event detection (for example, in images and video), host-based computer intrusion detection, detection of equipment or complex system failures, as well as of anomalous measurements in scientific experiments.

A classification system includes memory which stores, for each of a set of classes, a classifier model for assigning a class probability to a test sample from a target domain. The classifier model has been learned with training samples from the target domain and from at least one source domain. Each classifier model models the respective class as a mixture of components, the component mixture including a component for each source domain and a component for the target domain. Each component is a function of a distance between the test sample and a domain-specific class representation which is derived from the training samples of the respective domain that are labeled with the class, each of the components in the mixture being weighted by a respective mixture weight. Instructions, implemented by a processor, are provided for labeling the test sample based on the class probabilities assigned by the classifier models.

An adaptation method includes using a first classifier trained on projected representations of labeled objects from a first domain to predict pseudo-labels for unlabeled objects in a second domain, based on their projected representations. A classifier ensemble is iteratively learned. The ensemble includes a weighted combination of the first classifier and a second classifier. This includes training the second classifier on the original representations of the unlabeled objects for which a confidence for respective pseudo-labels exceeds a threshold. A classifier ensemble is constructed as a weighted combination of the first classifier and the second classifier. Pseudo-labels are predicted for the remaining original representations of the unlabeled objects with the classifier ensemble and weights of the first and second classifiers in the classifier ensemble are adjusted. As the iterations proceed, the unlabeled objects progressively receive pseudo-labels which can be used for retraining the second classifier.

A system and method for adapting a language model to a specific environment by receiving interactions captured the specific environment, generating a collection of documents from documents retrieved from external resources, detecting in the collection of documents terms related to the environment that are not included in an initial language model and adapting the initial language model to include the terms detected.

The invention discloses an adversarial-learning-based multi-source-domain adaptive migration method and system. The method comprises: step one, pre training is carried out by using all-source-domain data and a representation network and a classifier of a target model are initialized; step two, multi-path adversarial adversarial processing is carried out on multi-source-domain data and target-domain data and a representation network and a multi-path discriminator of the target model are updated; step three, adversarial scores between the source domains and the target domain are calculated; stepfour, target domain classification is carried out based on the classifiers and the adversarial scores of all source domains; step five, a target domain pseudo sample with a high confidence coefficient is selected for fine tuning of the representation network and the classifier of the target model; and step six, the steps from the step two to the step five are carried out again until model convergence is realized or a maximum iteration number of times is reached, and then training is stopped. According to the invention, reliance on the hypothesis of consistency of the single-source-domain tagset and the target domain is eliminated; and a negative migration phenomenon existing in the multi-source domain adaptation process is avoided effectively.

The invention discloses a vibration data fault classification method based on depth domain adaptation. The vibration data fault classification method comprises the steps of constructing a source domain containing a large amount of sample data and a target domain containing a small amount of sample data; constructing a classifier by using the sample data in the source domain; constructing paired samples according to the sample data in different domains in the source domain and the target domain; constructing a twin network, inputting the paired samples as training samples into the twin networkfor domain adaptation, and obtaining a final loss function of the paired samples; and optimizing the final loss function, and obtaining a trained fault classification model. According to the present invention, the problem that an existing deep network model is poor in diagnosis effect under the condition that fault sample data are insufficient is solved, a domain self-adaption method in deep learning and transfer learning is combined, the existing data are utilized to the maximum extent, the generalization capacity of the model is improved, and therefore better classification accuracy is obtained.

The invention provides a domain adaptive image semantic segmentation method based on antagonistic learning, which comprises the following main contents: domain adaptation, network structure and outputspace adaptation. The process comprises the following steps: firstly, inputting images of a source domain and a target domain to a segmentation network to predict the source domain and the target domain to obtain segmentation output; generating the segmentation loss of the source domain by the source prediction obtained from the source output. Then, two segmented outputs are used as the input ofthe discriminator to generate anti-loss, and then the anti-loss is transmitted to the segmented network. Finally, the pixel-level semantic image segmentation is achieved by minimizing the segmentationloss and maximizing the antagonistic loss. The invention develops a multi-level confrontation learning method, which can effectively align the scene layout and the local context between the source and the target images in the self-adaptation of the segmentation space. In addition, the invention is simple, convenient and easy to operate, and can well solve the influence of adapting to the complexity of the high-dimensional features.

Methods and systems for recognizing a license plate character. Synthetic license plate character images are generated for a target jurisdiction. A limited set of license plate images can be captured for a target jurisdiction utilizing an image-capturing unit. The license plate images are then segmented into license plate character images for the target jurisdiction. The license plate character images collected for the target jurisdiction can be manually labeled. A domain adaptation technique can be utilized to reduce the divergence between synthetically generated and manually labeled target jurisdiction image sets. Additionally, OCR classifiers are trained utilizing the images after the domain adaptation method has been applied. One or more input license plate character images can then be received from the target jurisdiction. Finally, the trained OCR classifier can be employed to determine the most likely labeling for the character image and a confidence associated with the label.

Systems and methods for efficient online domain adaptation are provided herein. Methods may include receiving a post-edited machine translated sentence pair, updating a machine translation model by adjusting translation weights for a translation memory and a language model while generating test machine translations of the machine translated sentence pair until one of the test machine translations approximately matches the post-edits for the machine translated sentence pair, and retranslating the remaining machine translation sentence pairs that have yet to be post-edited using the updated machine translation model.

The invention discloses a method for extracting clinical medical information based on a neural network. Generally, a medical text has much professional vocabulary, uncommon vocabulary and time expressions composed of numbers and characters, but character vectors obtained by a convolutional neural network may contain the morphological information of words, so the problem can be solved very well. Further, a bidirectional LSTM can capture context information well. In addition, the method of using the neural network avoids a process of artificially designing features in machine learning, and can well solve domain adaptation. The method of the invention achieves good results in different data fields, and can extract information with practical values and research significance from massive medical data efficiently and accurately.

The invention discloses a transfer learning design method and system based on domain adaptation under a multi-example multi-label framework. According to the invention, multi-example multi-label learning and transfer learning are unified into one framework, source domain data samples and target domain data samples are effectively utilized for correlation statistics, and the source domain samples can be effectively used in the learning of target domain tasks; the characteristics of a source domain data sample set and a target domain data set in RKHS are utilized, a two-step domain adaptation process formed by sample weighting and a sample selection mechanism based on clustering is utilized, so that the learning of target tasks has enough training samples weighted and selected from the source domain set; and a miFV algorithm is utilized to convert multi-examples into a single example, and the calculation cost problem of domain adaptation is solved.

Systems and methods for performing domain adaptation include collecting a labeled source image having a view of an object. Viewpoints of the object in the source image are synthesized to generate view augmented source images. Photometrics of each of the viewpoints of the object are adjusted to generate lighting and view augmented source images. Features are extracted from each of the lighting and view augmented source images with a first feature extractor and from captured images captured by an image capture device with a second feature extractor. The extracted features are classified using domain adaptation with domain adversarial learning between extracted features of the captured images and extracted features of the lighting and view augmented source images. Labeled target images are displayed corresponding to each of the captured images including labels corresponding to classifications of the extracted features of the captured images.

The invention discloses a target detection positioning optimization method based on unsupervised domain adaptation, which aims to solve the technical problem that an existing domain adaptation targetdetection method is insufficient in positioning capability for the position of an object under the condition that the object migrates from a labeled data set to an unlabeled data set. The method comprises the steps of (1) processing the data, (2) constructing a model, (3) defining a loss function, (4) training the model and (5) verifying the model. The present invention provides a new feature extraction network model which is suitable for the unlabeled data set, the positioning is more optimized, and the representation effect of the object positioning is good.

The invention discloses an unsupervised domain adaptation method based on an adversarial learning loss function, and the method comprises the steps: (1), generating a high-level feature of a source domain image through a feature extraction network G, carrying out the cross entropy loss with a real label through a classifier C, generating a confusion matrix through a domain discriminator D, and correcting a pseudo label into the real label; and (2) generating high-level features of the target domain image through a feature extraction network G, generating pseudo tags through a classifier C, generating a confusion matrix of the high-level features through a domain discriminator D, and correcting the pseudo tags to be in opposite distribution. (3) confronting and optimizing the loss functionby a feature generator and a discriminator. In addition, for the confusion matrix on the target domain, a correction label is generated and serves as a label of the target domain, and the classifier is optimized. By utilizing the method and the device, the noise of the pseudo tag can be corrected in unsupervised domain adaptation, and the distribution difference between the domains is matched, sothat the classification precision of the target domain is improved.

Systems and methods for domain adaptation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

A vehicle, system and method of navigating a vehicle. The vehicle and system include a digital camera for capturing a target image of a target domain of the vehicle, and a processor. The processor is configured to: determine a target segmentation loss for training the neural network to perform semantic segmentation of a target image in a target domain, determine a value of a pseudo-label of the target image by reducing the target segmentation loss while providing aa supervision of the training over the target domain, perform semantic segmentation on the target image using the trained neural network to segment the target image and classify an object in the target image, and navigate the vehicle based on the classified object in the target image.

A system for transfer learning and domain adaptation using distributable data models is provided, comprising a network-connected distributable model configured to serve instances of a plurality of distributable models; and a directed computation graph module configured to receive at least an instance of at least one of the distributable models from the network-connected computing system, create a second dataset from machine learning performed by a transfer engine, train the instance of the distributable model with the second dataset, and generate an update report based at least in part by updates to the instance of the distributable model.

The invention provides an open set domain adaptation method based on entropy minimization. The open set domain adaptation method comprises the steps of creating an open set domain adaptation network, and initializing network hyper-parameters; inputting a source domain picture and a target domain picture into an open set domain adaptive network, obtaining picture depth features through a networkfeature extraction layer, and then obtaining prediction probability vectors of various categories through a softmax layer according to a feature map; calculating an entropy loss function by using a known category prediction probability vector and a picture label so as to correctly identify a common category; on the basis, calculating binary cross entropy and a binary diversity loss function by using an unknown category prediction probability value so as to correctly classify unknown categories, updating network parameters by using a back propagation gradient until a loss function value is minimum, and stopping training the network; and storing the network model and the training result, and introducing the target domain data set into the network model to obtain a final target domain label.

The embodiment of the invention provides a transfer learning method and device, and the method comprises the steps: obtaining a preprocessed sub-sequence and a dense prediction network model, whereinthe preprocessed sub-sequence comprises a source domain sub-sequence and a target domain sub-sequence; Inputting the source domain sub-sequence and the target domain sub-sequence into a dense prediction network model in batches to obtain global shift loss; Obtaining a source domain label and a target domain pseudo label, and performing corresponding category centroid alignment according to the source domain label and the target domain pseudo label on the basis of a cosine distance loss and moving average method to obtain corresponding category shift loss; And obtaining total loss according tothe global shift loss and the corresponding category shift loss, and updating the dense prediction network model according to the total loss. According to the migration learning method and device provided by the embodiment of the invention, a multi-level unsupervised domain adaptation method is provided, alignment of edge distribution and condition distribution is completed, migration of a time series data intensive prediction model is realized, and the performance is more superior.