Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition
Abstract: Deep neural networks (DNNs) have achieved significant success in various applications with large-scale and balanced data. However, data in real-world visual recognition are usually long-tailed, bringing challenges to efficient training and deployment of DNNs. Information bottleneck (IB) is an elegant approach for representation learning. In this paper, we propose a balanced information bottleneck (BIB) approach, in which loss function re-balancing and self-distillation techniques are integrated into the original IB network. BIB is thus capable of learning a sufficient representation with essential label-related information fully preserved for long-tailed visual recognition. To further enhance the representation learning capability, we also propose a novel structure of mixture of multiple balanced information bottlenecks (MBIB), where different BIBs are responsible for combining knowledge from different network layers. MBIB facilitates an end-to-end learning strategy that trains representation and classification simultaneously from an information theory perspective. We conduct experiments on commonly used long-tailed datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018. Both BIB and MBIB reach state-of-the-art performance for long-tailed visual recognition.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=w4HU3jZ9rQ
Changes Since Last Submission: 1. Revise and enhance the writing of this paper. Provide more detailed explanations for certain terms and variables mentioned.
2. Perform additional experiments and analyses to evaluate the impact of the observation $v$ quantity.
Assigned Action Editor: ~Yin_Cui1
Submission Number: 3977
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