Transformers Meet Small Datasets

The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets. In this work, we first propose a new hybrid model by combining the transformer and convolution neural network (CNN). The proposed model improves the classification ability on small datasets. This is accomplished by introducing more convolution operations in the transformer's two core sections: 1) Instead of the original multi-head attention mechanism, we design a convolutional parameter sharing multi-head attention (CPSA) block that incorporates the convolutional parameter sharing projection in the attention mechanism; 2) the feed-forward network in each transformer encoder block is replaced with a local feed-forward network (LFFN) block that introduces a sandglass block with more depth-wise convolutions to provide more locality to the transformers. We achieve state-of-the-art results when training from scratch on 4 small datasets as compared with the transformers and CNNs without extensive computing resources and auxiliary training. The proposed strategy opens up new paths for the application of transformers on small datasets.

Automated summary

This paper proposes a hybrid model that combines Transformer and CNN to improve the classification ability of Transformers on small datasets. By introducing more convolution operations, the model achieves state-of-the-art results on 4 small datasets, opening up new paths for the application of Transformers on small datasets.