Alias-Free ViT: Fractional Shift Invariance via Linear Attention
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914116083384320 |
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| author | Michaeli, Hagay Soudry, Daniel |
| author_facet | Michaeli, Hagay Soudry, Daniel |
| contents | Transformers have emerged as a competitive alternative to convnets in vision tasks, yet they lack the architectural inductive bias of convnets, which may hinder their potential performance. Specifically, Vision Transformers (ViTs) are not translation-invariant and are more sensitive to minor image translations than standard convnets. Previous studies have shown, however, that convnets are also not perfectly shift-invariant, due to aliasing in downsampling and nonlinear layers. Consequently, anti-aliasing approaches have been proposed to certify convnets' translation robustness. Building on this line of work, we propose an Alias-Free ViT, which combines two main components. First, it uses alias-free downsampling and nonlinearities. Second, it uses linear cross-covariance attention that is shift-equivariant to both integer and fractional translations, enabling a shift-invariant global representation. Our model maintains competitive performance in image classification and outperforms similar-sized models in terms of robustness to adversarial translations. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_22673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alias-Free ViT: Fractional Shift Invariance via Linear Attention Michaeli, Hagay Soudry, Daniel Computer Vision and Pattern Recognition Transformers have emerged as a competitive alternative to convnets in vision tasks, yet they lack the architectural inductive bias of convnets, which may hinder their potential performance. Specifically, Vision Transformers (ViTs) are not translation-invariant and are more sensitive to minor image translations than standard convnets. Previous studies have shown, however, that convnets are also not perfectly shift-invariant, due to aliasing in downsampling and nonlinear layers. Consequently, anti-aliasing approaches have been proposed to certify convnets' translation robustness. Building on this line of work, we propose an Alias-Free ViT, which combines two main components. First, it uses alias-free downsampling and nonlinearities. Second, it uses linear cross-covariance attention that is shift-equivariant to both integer and fractional translations, enabling a shift-invariant global representation. Our model maintains competitive performance in image classification and outperforms similar-sized models in terms of robustness to adversarial translations. |
| title | Alias-Free ViT: Fractional Shift Invariance via Linear Attention |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.22673 |