10 results about "Discriminative learning" patented technology

This invention proposes an image multi-task recognition method and system based on advanced learning, constructing an end-to-end advanced multi-task deep learning network model for simultaneous facial attribute recognition. This advanced multi-task learning network consists of three main modules: a primary network, an advanced network, and an expert-level network. The primary network is constructed using a lifelong learning strategy, training on facial attribute samples to acquire basic facial attribute recognition capabilities. The advanced network is constructed using a multi-task learning strategy, training on specially collected facial multi-attribute samples for multi-task collaborative learning, enabling advanced recognition of multiple facial attributes simultaneously. The expert-level network is constructed using a soft-label learning strategy, training on specially collected, confidence-based facial multi-attribute training samples for discriminative learning, enabling expert-level identification capabilities that see beyond appearances.

The application relates to the technical field of natural languages, and discloses a question generation method, which comprises the following steps: acquiring a question data set, wherein the question data set comprises initial questions; generating questions by using a question generator to obtain candidate questions; generating answers by using an answer generator to obtain candidate answers and question answers; determining first similarity values and second similarity values according to all question answers corresponding to the same initial question and candidate answers corresponding to the same candidate question; and screening all candidate questions according to all first similarity values and all second similarity values to obtain target questions. Through discriminative learning on the adversarial questions, the learning result is fed back to the question generator for optimization, so that the question generation model has a higher accuracy in generating questions, and the accuracy of artificial customer service in replying to customer questions in the insurance field or the financial field is improved.

The invention relates to the technical field of artificial intelligence, and provides a visual basic model training and visual feature extraction method, device and equipment, and the method comprises the steps: carrying out the training of a visual basic model based on the reconstruction prediction result of each image, the original image information of each image, and the classification prediction result and the classification false label of each image; and performing parameter iteration on the visual feature extraction model, and taking the initial visual basic model after parameter iteration as a visual basic model. According to the method, a classification task of discriminant learning and an image reconstruction task of generative learning are combined in a unified training framework, so that the visual basic model obtained by training retains rich local structures and detail representations learned by a generative model, and the accuracy of image reconstruction of the visual basic model is improved; and meanwhile, the high-level semantic understanding capability of the discriminant of the visual basic model is also improved, so that the method has excellent few-sample migration performance in tasks such as image classification and the like, and the deployment cost of the visual basic model is reduced.

The application relates to a scene-dependent video anomaly detection method and device. The method comprises: acquiring events and scene features. After feature preprocessing, random disturbance is applied to the scene features, and the event features are updated through time dimension self-attention. Modulation parameters are generated through a scene condition fusion block, the event features are modulated and fused, and residual connection is performed, a scene condition classification head is input, and discriminative learning is performed with a camera ID as a label. Meanwhile, the features are processed for comparative learning, positive and negative sample pairs are constructed in combination with a memory bank, and bidirectional symmetric comparative learning is performed. Two types of anomaly scores are calculated, adaptive fusion is performed based on distribution difference and local correlation, the maximum fusion score of each frame is taken as a frame-level score, and output is performed after median filtering and smoothing, so that accurate anomaly detection is realized. The method can improve the detection accuracy and practicability in a complex multi-scene environment.

The system receives a plurality of medical images and integrates Self-Supervised machine Learning (SSL) instructions for performing a discriminative learning operation, a restorative learning operation, and an adversarial learning operation into a model for processing the received plurality of medical images. The model is configured with each of a discriminative encoder, a restorative decoder, and an adversarial encoder. Each of the discriminative encoder and the restorative decoder are configured to be skip connected, forming an encoder-decoder. Step-wise incremental training to incrementally train each of the discriminative encoder, the restorative decoder, and the adversarial encoder is performed, in particular: pre-training the discriminative encoder via discriminative learning; attaching the pre-trained discriminative encoder to the restorative decoder to configure the encoder-decoder as a pre-trained encoder-decoder; and training the pre-trained encoder-decoder of the model using joint discriminative and restorative learning. The pre-trained encoder-decoder is associated with the adversarial encoder. The pre-trained encoder-decoder associated with the adversarial encoder is trained through discriminative, restorative, and adversarial learning to render a trained model for the processing of the received plurality of medical images. The plurality of medical images are processed through the model using the trained model.

In various examples, a technique for performing unified generative and discriminative learning includes converting, via execution of a machine learning model, a plurality of training data samples into a first plurality of latent representations. The technique also includes computing one or more losses based on the plurality of latent representations, wherein the loss(es) include a contrastive term that approximates an expected similarity between a latent representation of a training data sample and a second plurality of latent representations associated with a distribution of training data samples that includes the plurality of training data samples. The technique further includes updating one or more parameters of the machine learning model based on the one or more losses to generate a trained machine learning model.

The application discloses a weakly supervised temporal action localization method based on mask pyramid enhancement, and belongs to the fields of computer vision and action localization. System modules for realizing the method include a feature extraction module, a mask pyramid classification module MPCM for action integrity learning, and an attention module FDAM for feature discriminative learning. The feature extraction module is used for feature extraction and serves as input data of subsequent links; the MPCM constructs a refined enhanced mask pyramid by using a temporal adaptive enhancement mechanism TAEM, and introduces a mask strategy driven model in hierarchical processing to mine new feature regions complementary to a target action. Perception fusion is performed on classification results of different levels to generate an enhanced class activation sequence CAS. The MPENet introduces an exclusive loss in the FDAM, helps the model focus on key parts of the target action, thereby guiding the model to learn appropriate attention distribution, and improving the understanding and distinguishing ability of the model to features. Finally, the CAS is weighted with attention to obtain a prediction result of the model.

The application provides a layered robust discriminant learning method for hyperspectral remote sensing image anomaly detection. 1，1 The application effectively depicts the complex mixed noise introduced in the process of acquiring the hyperspectral remote sensing image in a real scene through the l norm and the Frobenius norm, and improves the anti-noise performance of the anomaly target detection model. In order to more accurately separate the deeply mixed background and anomaly target, obtain a robust and more powerful anomaly target detection model, and design a layered detection idea, the background and anomaly target components in the deeply mixed hyperspectral remote sensing image are gradually separated. The application can not only improve the distinguishability between the background and the anomaly target, but also has very strong noise suppression performance and detection robustness.

The system receives a plurality of medical images and integrates Self-Supervised machine Learning (SSL) instructions for performing a discriminative learning operation, a restorative learning operation, and an adversarial learning operation into a model for processing the received plurality of medical images. The model is configured with each of a discriminative encoder, a restorative decoder, and an adversarial encoder. Each of the discriminative encoder and the restorative decoder are configured to be skip connected, forming an encoder-decoder. Step-wise incremental training to incrementally train each of the discriminative encoder, the restorative decoder, and the adversarial encoder is performed, in particular: pre-training the discriminative encoder via discriminative learning; attaching the pre-trained discriminative encoder to the restorative decoder to configure the encoder-decoder as a pre-trained encoder-decoder; and training the pre-trained encoder-decoder of the model using joint discriminative and restorative learning. The pre-trained encoder-decoder is associated with the adversarial encoder. The pre-trained encoder-decoder associated with the adversarial encoder is trained through discriminative, restorative, and adversarial learning to render a trained model for the processing of the received plurality of medical images. The plurality of medical images are processed through the model using the trained model.

The application discloses a human action recognition method based on semantic perception fusion and adaptive discriminative learning, first action description semantic generation and coding; then video feature coding; finally, based on an adaptive mechanism and a contrast learning classifier, human action recognition is finally realized. The application solves the problem that the existing method based on an RGB video is often limited by background interference, motion blur and semantic inconsistency caused by modal heterogeneity, so that the recognition performance is difficult to meet the actual demand.