Improved Masked Image Generation with Token-Critic
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Cited by 21 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Non-autoregressive generative transformers recently demonstrated impressive image generation performance, and orders of magnitude faster sampling than their autoregressive counterparts. However, optimal parallel sampling from the true joint distribution of visual tokens remains an open challenge. In this paper we introduce Token-Critic, an auxiliary model to guide the sampling of a non-autoregressive generative transformer. Given a masked-and-reconstructed real image, the Token-Critic model is trained to distinguish which visual tokens belong to the original image and which were sampled by the generative transformer. During non-autoregressive iterative sampling, Token-Critic is used to select which tokens to accept and which to reject and resample. Coupled with Token-Critic, a state-of-the-art generative transformer significantly improves its performance, and outperforms recent diffusion models and GANs in terms of the trade-off between generated image quality and diversity, in the challenging class-conditional ImageNet generation.
Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Builds on 36 of its 41 references that are in the map. First posted on arXiv 9 Sep 2022.
Builds on
36 of 41 references.
References · 36
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Built on by
Citing papers per year: 2022: 3; 2023: 14; 2024: 20; 2025: 16; 2026: 10.
63 papers.
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- 2025A Semantic Information-based Hierarchical Speech Enhancement Method Using Factorized Codec and Diffusion Model20 May 2025
- 2024Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion18 Oct 2024
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- 2026Accelerating Diffusion Decoders via Multi-Scale Sampling and One-Step Distillation20 Mar 2026
- 2026Soft Tail-dropping for Adaptive Visual Tokenization20 Jan 2026
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