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Unified Visual Transformer Compression
[article]
2022
arXiv
pre-print
Vision transformers (ViTs) have gained popularity recently. Even without customized image operators such as convolutions, ViTs can yield competitive performance when properly trained on massive data. However, the computational overhead of ViTs remains prohibitive, due to stacking multi-head self-attention modules and else. Compared to the vast literature and prevailing success in compressing convolutional neural networks, the study of Vision Transformer compression has also just emerged, and
arXiv:2203.08243v1
fatcat:5rrj5vn53zdahejoxtfaoda6me