Cell Variational Information Bottleneck Network

• URL: http://arxiv.org/abs/2403.15082v3
• Date: Fri, 29 Mar 2024 07:20:42 GMT
• Title: Cell Variational Information Bottleneck Network
• Authors: Zhonghua Zhai, Chen Ju, Jinsong Lan, Shuai Xiao,
• Abstract summary: We propose a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture. Cell Variational Information Bottleneck Network is constructed by stacking VIB cells, which generate feature maps with uncertainty. In a more complex representation learning task, face recognition, our network structure has also achieved very competitive results.
• Score: 6.164295534465283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture in an end-to-end training method. Our Cell Variational Information Bottleneck Network is constructed by stacking VIB cells, which generate feature maps with uncertainty. As layers going deeper, the regularization effect will gradually increase, instead of directly adding excessive regular constraints to the output layer of the model as in Deep VIB. Under each VIB cell, the feedforward process learns an independent mean term and an standard deviation term, and predicts the Gaussian distribution based on them. The feedback process is based on reparameterization trick for effective training. This work performs an extensive analysis on MNIST dataset to verify the effectiveness of each VIB cells, and provides an insightful analysis on how the VIB cells affect mutual information. Experiments conducted on CIFAR-10 also prove that our cellVIB is robust against noisy labels during training and against corrupted images during testing. Then, we validate our method on PACS dataset, whose results show that the VIB cells can significantly improve the generalization performance of the basic model. Finally, in a more complex representation learning task, face recognition, our network structure has also achieved very competitive results.

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