Two-stage self-supervised training vision transformers for small datasets

Jiaxian Yang; Taiwei Cai; Haojie Chen

doi:10.1117/12.3037879

12 September 2024 Two-stage self-supervised training vision transformers for small datasets

Jiaxian Yang, Taiwei Cai, Haojie Chen

Author Affiliations +

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 132560C (2024) https://doi.org/10.1117/12.3037879
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

After a series of achievements in the field of natural language processing, Transformers have shown promising results upon their introduction to computer vision, particularly under conditions of large-scale data. However, when faced with insufficient data, the performance of Vision Transformers(ViT) often falls short compared to Convolutional Neural Networks (CNNs), which are capable of capturing intrinsic biases in the data. In this paper, to address the performance disadvantage of ViT on small datasets, we propose a two-stage self-supervised training strategy. We enhance the ViT model by introducing Sequential Overlapping Patch Embedding (SOPE) and Improved Dynamic Aggregation Feed Forward (IDAFF) modules. Applying our approach to both single-block and multi-block ViT models on five commonly used small datasets (CIFAR10, CIFAR100, CINIC10, SVHN, Tiny-ImageNet) shows the efficacy of our approach. It narrows the performance difference between ViT and CNNs during training on small datasets from the ground up, and in some cases, even achieves better classification performance than CNNs. Our codes are available at: https://github.com/newer7/vosd.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaxian Yang, Taiwei Cai, and Haojie Chen "Two-stage self-supervised training vision transformers for small datasets", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 132560C (12 September 2024); https://doi.org/10.1117/12.3037879

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KEYWORDS

Education and training

Machine learning

Data modeling

Performance modeling

Transformers

Image enhancement

Visual process modeling

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