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arXiv Computer Vision By Zhang Xiaofeng, Samuel Lavoie, Nicolas Beltran-Velez, Jianhao Yuan, Felix Friedrich, Jakob Verbeek, Aaron Courville, Michal Drozdzal, Adriana Romero-Soriano

FeaTok: Feature-Loss Tokenization for Image Generation

Read the original on arXiv Computer Vision →

FeaTok introduces feature-loss tokenization for image generation, training discrete tokens by reconstructing pretrained features instead of pixels. This approach improves generative performance across multiple self-supervised models (SigLIP, SigLIP2, DINOv2, DINOv3). In class-conditional generation, FeaTok reduces gFDr6 by up to 29% at 256×256 resolution and outperforms the strongest unguided continuous baseline with 38× less sampling compute, while also achieving higher human-preference reward-model scores and surpassing text-conditional baselines after only 500 fine-tuning updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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