Abstract
Inspired by Transformer, one of the most successful deep learning models in natural language processing, machine translation, etc. Vision Transformer (ViT) has recently demonstrated its effectiveness in computer vision tasks such as image classification, object detection, etc. However, the major issue with ViT is to require massively trainable parameters. In this paper, we propose a novel compressed ViT model, namely Tensor-train ViT (TT-ViT), based on tensor-train (TT) decomposition. Consider a multi-head self-attention layer, instead of storing whole trainable matrices, we represent them in TT format via their TT cores using fewer parameters. The results of our experiments on CIFAR-10/Fashion-MNIST dataset reveal that TT-ViT achieves outstanding performance with equivalent accuracy to its baseline model, while total parameters of TT-ViT are just half of those of the baseline model.
This research is funded by University of Science, VNU-HCM under grant number CNTT 2020-09.
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This research is funded by University of Science, VNU-HCM under grant number CNTT 2020-09.
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Pham Minh, H., Nguyen Xuan, N., Tran Thai, S. (2022). TT-ViT: Vision Transformer Compression Using Tensor-Train Decomposition. In: Nguyen, N.T., Manolopoulos, Y., Chbeir, R., Kozierkiewicz, A., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2022. Lecture Notes in Computer Science(), vol 13501. Springer, Cham. https://doi.org/10.1007/978-3-031-16014-1_59
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