Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets

TL;DR

This paper introduces a Dynamic Hybrid Vision Transformer (DHVT) that integrates convolutional structures and dynamic channel aggregation to improve ViT performance on small datasets, closing the gap with CNNs.

Contribution

The paper proposes DHVT, a novel hybrid model that enhances spatial relevance and channel diversity in ViTs, achieving state-of-the-art results on small datasets.

Findings

DHVT achieves 85.68% on CIFAR-100 with 22.8M parameters.

DHVT attains 82.3% on ImageNet-1K with 24.0M parameters.

DHVT outperforms previous ViT models on small datasets.

Abstract

There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive biases, that is, the spatial relevance and diverse channel representation. First, on spatial aspect, objects are locally compact and relevant, thus fine-grained feature needs to be extracted from a token and its neighbors. While the lack of data hinders ViTs to attend the spatial relevance. Second, on channel aspect, representation exhibits diversity on different channels. But the scarce data can not enable ViTs to learn strong enough representation for accurate recognition. To this end, we propose Dynamic Hybrid Vision Transformer (DHVT) as the solution to enhance the two inductive biases. On spatial aspect, we adopt a hybrid structure, in which convolution is integrated into patch embedding and multi-layer perceptron module, forcing the model to capture the token features as well as their neighboring features. On channel aspect, we introduce a dynamic feature aggregation module in MLP and a brand new "head token" design in multi-head self-attention module to help re-calibrate channel representation and make different channel group representation interacts with each other. The fusion of weak channel representation forms a strong enough representation for classification. With this design, we successfully eliminate the performance gap between CNNs and ViTs, and our DHVT achieves a series of state-of-the-art performance with a lightweight model, 85.68% on CIFAR-100 with 22.8M parameters, 82.3% on ImageNet-1K with 24.0M parameters. Code is available at https://github.com/ArieSeirack/DHVT.