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Discrete Representations Strengthen Vision Transformer Robustness

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arxiv 2111.10493 v2 pith:U5G67EGI submitted 2021-11-20 cs.CV

Chengzhi Mao , Lu Jiang , Mostafa Dehghani , Carl Vondrick , Rahul Sukthankar , Irfan Essa This is my paper · ORCID
classification cs.CV
keywords discretevitsarchitectureimagenetinformationrobustnesstokensadding
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Vision Transformer (ViT) is emerging as the state-of-the-art architecture for image recognition. While recent studies suggest that ViTs are more robust than their convolutional counterparts, our experiments find that ViTs trained on ImageNet are overly reliant on local textures and fail to make adequate use of shape information. ViTs thus have difficulties generalizing to out-of-distribution, real-world data. To address this deficiency, we present a simple and effective architecture modification to ViT's input layer by adding discrete tokens produced by a vector-quantized encoder. Different from the standard continuous pixel tokens, discrete tokens are invariant under small perturbations and contain less information individually, which promote ViTs to learn global information that is invariant. Experimental results demonstrate that adding discrete representation on four architecture variants strengthens ViT robustness by up to 12% across seven ImageNet robustness benchmarks while maintaining the performance on ImageNet.

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Forward citations

Cited by 5 Pith papers

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  1. On the Role of Discrete Representation in Sparse Mixture of Experts

    cs.LG 2024-11 reject novelty 6.0 of 10

    VQMoE is a routing-free SMoE variant using vector-quantized discrete codes for expert selection; its empirical gains are small and its theoretical proofs are circular or unsupported.

  2. ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers

    cs.CV 2025-08 conditional novelty 5.0 of 10

    ViT-EnsembleAttack augments each ViT surrogate with three randomized strategies, tunes their parameters by Bayesian optimization, and ensembles them to substantially improve adversarial transferability.

  3. Derivations of rational vertex operator algebras are inner

    math.QA 2026-06 unverdicted novelty 4.0 of 10

    Every derivation of a simple rational VOA of CFT type is an inner derivation.

  4. MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization

    cs.CV 2025-07 conditional novelty 4.0 of 10

    Splitting quantization across multiple small sub-codebooks with nested masking raises VQ-VAE reconstruction fidelity, giving MGVQ rFID 0.49 and PSNR 24.70 on ImageNet at 16 times downsampling.

  5. [MASK] is All You Need

    cs.CV 2024-12 conditional novelty 4.0 of 10

    Discrete Interpolants frames image generation, segmentation, and video generation as unmasking discrete [MASK] tokens, connecting masked generative models and discrete diffusion models.

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