4 results about "Multi label learning" patented technology

The invention relates to an incomplete multi-view multi-label classification method based on cross-view distillation and adaptive mask. The method comprises the following steps: acquiring incomplete multi-view multi-label data; and inputting the incomplete multi-view multi-label data into a multi-label classification model, extracting multi-view depth representation, performing mask filtering and fusion on low-quality representation, and inputting the fused representation into a multi-label classifier to obtain a corresponding prediction result, the multi-label classification model is obtained through training of incomplete multi-view multi-label training data, and multi-view distillation and a self-adaptive shielding method are fused in the training process. According to the method, multi-view distillation and adaptive shielding technologies are fused, and the inherent unbalance and noise problems in an incomplete multi-view multi-label learning task are solved.

The invention discloses a plate strip steel surface defect detection method based on partial multi-label learning, and belongs to the technical field of industrial quality detection and data mining. In order to solve the problem that in the prior art, due to the fact that labeling noise is difficult to eliminate, model robustness is insufficient, a semantic alignment mechanism is introduced, and collaborative modeling is carried out on a sample feature space and a label semantic space. The process comprises the following steps: in a data preparation stage, extracting plate strip steel image features and constructing candidate tags; in the label denoising stage, a partial multi-label learning framework is adopted, label false correlation is eliminated through orthogonal rotation, label reliability is improved through joint projection, a label relation is reconstructed through manifold learning, and finally a denoised discrimination label is obtained. In the classifier training stage, a depth perception classifier is trained by using a discriminant label and an original label; and in the detection stage, defects such as cracks and scratches are identified. According to the method, the detection precision and the anti-interference capability are improved, the dependence on manual labeling is reduced, and reliable support is provided for plate and strip steel quality control.

The present application relates to the technical field of information security, and in particular to a multi-label learning based side-channel analysis method for different devices, which first proposes a shortest principle to form the energy traces corresponding to the single-byte key labels of each device according to the characteristics of the energy trace data sets collected under different devices and the requirements for deep learning training, and forms a multi-label data set for different devices, then applies the multi-label learning technology to the side-channel environment, realizes the algorithm adaptation method of multi-label learning based on a convolutional neural network, further sets various hyperparameter combinations to optimize the multi-label learning model, and finally uses the corresponding test sets under each device to evaluate the byte key attack effect of the multi-label model on the cryptographic algorithm under different devices, so as to verify the generalization ability of the multi-label learning model. The present application enables a single model to perform attacks on the same cryptographic algorithm under different devices, increases the attack efficiency, and reduces the model construction and training time.

The application relates to a multi-label learning method, device and equipment based on sample missing label enhancement, which comprises the following steps: acquiring a training data set of a missing label sample; pre-processing the training data set to obtain a processed training set with restored real labels; learning and aggregating the processed training set by using an algorithm adaptation strategy to obtain a multi-label learning classifier; taking the classifier as a label prediction model; and inputting a sample to be predicted into the label prediction model to obtain labels corresponding to the sample to be predicted. The method realizes label information enhancement by obtaining the processed training set with restored real labels; then the processed training set is induced by using the algorithm adaptation strategy to obtain a classifier considering the class imbalance problem in the processed training set; and the label prediction model is constructed based on the classifier to solve the multi-label class imbalance problem and improve the precision and accuracy of the predicted labels.