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Yingshan Chang

7 accepted papers

2024

Diffusion PID: Interpreting Diffusion via Partial Information Decomposition

NeurIPS 2024poster

Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex visual-semantic relationships. While these diffusion models have achieved remarkable success, the underlying mechanisms dri…

2024

Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching

NeurIPS 2024poster

Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the instantaneous change-of-variables formula, one can directly compute image likelihoods from a learned flow, making them ent…

2024

VISREAS: Complex Visual Reasoning with Unanswerable Questions

ACL 2024findings

Verifying a question’s validity before answering is crucial in real-world applications, where users may provide imperfect instructions. In this scenario, an ideal model should address the discrepancies in the query and convey them to the users rather than generating the best possible answer. Address…

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