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cs.CV2026
FeaTok: Feature-Loss Tokenization for Image Generation
Zhang Xiaofeng, Samuel Lavoie, Nicolas Beltran-Velez +6
Discrete image generators learn the distribution of tokens produced by an image tokenizer. These token representations should be easy for generators to model, not merely enable fai…
cs.CV2025
The Intricate Dance of Prompt Complexity, Quality, Diversity, and Consistency in T2I Models
Zhang Xiaofeng, Aaron Courville, Michal Drozdzal +1
Text-to-image (T2I) models offer great potential for creating virtually limitless synthetic data, a valuable resource compared to fixed and finite real datasets. Previous works eva…
cs.CV2025
Bias Analysis in Unconditional Image Generative Models
Xiaofeng Zhang, Michelle Lin, Simon Lacoste-Julien +2
The widespread adoption of generative AI models has raised growing concerns about representational harm and potential discriminatory outcomes. Yet, despite growing literature on th…