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Knowledge distillation is an extensively researched model compression technology, which uses a large teacher network to transmit information to a small student network. The critical point of the knowledge distillation method to improve the performance of the student network is to find an effective method to extract the information from the feature. The attention mechanism is a widely used feature processing method to process features effectively and obtain more expressive information. In this paper, we propose to use the dual attention mechanism in knowledge distillation to improve the performance of student networks, which extracts information from the spatial and channel dimensions of the feature. The channel dimension attention is search 'what' channel is more meaningful, and the spatial dimension attention is determine 'where' part of the feature is more expressive in a feature map. We have conducted extensive experiments on different datasets, shown that by implementing a dual attention mechanism to extract more expressive information for knowledge transfer, the student network can achieve performance beyond the teacher network.

Semi-supervised learning (SSL) has become a crucial approach in deep learning as a way to address the challenge of limited labeled data. The success of deep neural networks heavily relies on the availability of large-scale high-quality labeled data. However, the process of data labeling is time-consuming and unscalable, leading to shortages in labeled data. SSL aims to tackle this problem by leveraging additional unlabeled data in the training process. One of the popular SSL algorithms, FixMatch [1], trains identical weight-sharing teacher and student networks simultaneously using a siamese neural network (SNN). However, it is prone to performance degradation when the pseudo labels are heavily noisy in the early training stage. We present KD-FixMatch, a novel SSL algorithm that addresses the limitations of FixMatch by incorporating knowledge distillation. The algorithm utilizes a combination of sequential and simultaneous training of SNNs to enhance performance and reduce performance degradation. Firstly, an outer SNN is trained using labeled and unlabeled data. After that, the network of the well-trained outer SNN generates pseudo labels for the unlabeled data, from which a subset of unlabeled data with trusted pseudo labels is then carefully created through high-confidence sampling and deep embedding clustering. Finally, an inner SNN is trained with the labeled data, the unlabeled data, and the subset of unlabeled data with trusted pseudo labels. Experiments on four public data sets demonstrate that KD-FixMatch outperforms FixMatch in all cases. Our results indicate that KD-FixMatch has a better training starting point that leads to improved model performance compared to FixMatch.

The application of deep learning (DL) in various brain computer interface (BCI) systems has achieved great success, but the results on the attention classification task are still not satisfactory. In this paper, an end-to-end mixed neural network model was proposed to classify the attention and nonattention mental states from multi-channel electroencephalography (EEG) data. During the experiment, a cross-subject strategy was performed on the attention detection task. Evaluated on a different electrodes combination of a publicly available dataset, the proposed model outperforms these baseline methods while maintaining relatively low computational complexity. The improved performance is meaningful for the attentive mental state classification task and is useful for the process of attention enhancement.

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