提出BIB与MBIB，解决长尾视觉识别中的信息丢失问题。
Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition
- 用重加权和自蒸馏改进信息瓶颈，保持标签关键信息。
- 在CIFAR100-LT、ImageNet-LT等数据集上达最新性能。
- 适合长尾数据下的模型训练与部署场景。
深度神经网络在大规模均衡数据上表现优异，但现实视觉识别数据常呈长尾分布，给DNN的高效训练与部署带来挑战。信息瓶颈（IB）是表示学习的有效方法。本文提出平衡信息瓶颈（BIB），将损失重加权与自蒸馏技术融入原始IB网络，使模型能充分保留与标签相关的关键信息，适用于长尾视觉识别。为进一步提升表示学习能力，提出多平衡信息瓶颈混合结构（MBIB），各BIB负责融合不同网络层的知识，支持从信息论视角端到端联合训练表示与分类。在常见长尾数据集CIFAR100-LT、ImageNet-LT和iNaturalist 2018上实验表明，BIB与MBIB均达到当前最优性能。
原文摘要 · Abstract (English)
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.
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