Yunchuan Guan — accepted AI-conference papers

2026

Learning to Learn Weight Generation via Local Consistency Diffusion

Yunchuan Guan, Yu Liu, Ke Zhou, Zhiqi Shen, Jenq-Neng Hwang, Lei Li

CVPR 2026

Diffusion-based algorithms have emerged as promising techniques for weight generation. However, existing solutions are limited by two challenges: generalizability and missing local supervision targets. The first challenge stems from the inherent lack of cross-task transferability in existing single-

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2025

Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy

Yunchuan Guan, Yu Liu, Ke Zhou, Zhiqi Shen, Jenq-Neng Hwang, Serge Belongie +1

ICCV 2025poster

Meta-learning is a powerful paradigm for tackling few-shot tasks. However, recent studies indicate that models trained with the whole-class training strategy can achieve comparable performance to those trained with meta-learning in few-shot classification tasks. To demonstrate the value of meta-lear…

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