Aggregating Nested Transformers

• URL: http://arxiv.org/abs/2105.12723v1
• Date: Wed, 26 May 2021 17:56:48 GMT
• Title: Aggregating Nested Transformers
• Authors: Zizhao Zhang, Han Zhang, Long Zhao, Ting Chen, Tomas Pfister
• Abstract summary: We explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical manner. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. Our empirical results show that the proposed method NesT converges faster and requires much less training data to achieve good generalization.
• Score: 42.96279765218623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although hierarchical structures are popular in recent vision transformers, they require sophisticated designs and massive datasets to work well. In this work, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical manner. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture with minor code changes upon the original vision transformer and obtains improved performance compared to existing methods. Our empirical results show that the proposed method NesT converges faster and requires much less training data to achieve good generalization. For example, a NesT with 68M parameters trained on ImageNet for 100/300 epochs achieves $82.3\%/83.8\%$ accuracy evaluated on $224\times 224$ image size, outperforming previous methods with up to $57\%$ parameter reduction. Training a NesT with 6M parameters from scratch on CIFAR10 achieves $96\%$ accuracy using a single GPU, setting a new state of the art for vision transformers. Beyond image classification, we extend the key idea to image generation and show NesT leads to a strong decoder that is 8$\times$ faster than previous transformer based generators. Furthermore, we also propose a novel method for visually interpreting the learned model.

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