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公开(公告)号:US20220351043A1
公开(公告)日:2022-11-03
申请号:US17448934
申请日:2021-09-27
申请人: Chongqing University , University of Electronic Science and Technology of China , Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. , Star Institute of Intelligent Systems
发明人: Yongduan Song , Feng Yang , Rui Li , Shengtao Pan , Siyu Li , Yiwen Zhang , Jian Zhang , Zhengtao Yu , Shichun Wang
摘要: The present disclosure discloses an adaptive high-precision compression method and system based on a convolutional neural network model, and belongs to the fields of artificial intelligence, computer vision, and image processing. According to the method of the present disclosure, coarse-grained pruning is performed on a neural network model by using a differential evolution algorithm first, and the coarse-grained space is quickly searched through an entropy importance criterion and an objective function with good guidance to obtain a near-optimal neural network structure. Then fine-grained search space is built on the basis of an optimal individual obtained from the coarse-grained search, and fine-grained pruning is performed on the neural network model by a differential evolution algorithm to obtain a network model with an optimal structure. Finally, the performance of the optimal model is restored by using a multi-teacher multi-step knowledge distillation network to reach the precision of an original model.
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公开(公告)号:US20220327308A1
公开(公告)日:2022-10-13
申请号:US17448926
申请日:2021-09-27
申请人: Chongqing University , University of Electronic Science and Technology of China , Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. , Star Institute of Intelligent Systems
发明人: Yongduan Song , Feng Yang , Rui Li , Yiwen Zhang , Haoyuan Zhong , Jian Zhang , Shengtao Pan , Siyu Li , Zhengtao Yu
摘要: The present disclosure relates to a method for recognizing facial expressions based on adversarial elimination. First, a facial expression recognition network is built based on a deep convolutional neural network. On a natural facial expression data set, the facial expression recognition network is trained through a loss function to make facial expression features easier to distinguish. Then some key features of input images are actively eliminated by using an improved confrontation elimination method to generate a new data set to train new networks with different weight distributions and feature extraction capabilities, forcing the network to perform expression classification discrimination based on more features, which reduces the influence of interference factors such as occlusion on the network recognition accuracy rate, and improving the robustness of the facial expression recognition network. Finally, the final expression classification predicted results are obtained by using network integration and a relative majority voting method.
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