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Learning an Efficient Optimizer via Hybrid-Policy Sub-Trajectory Balance
Yunchuan Guan, Yu Liu, Ke Zhou +8
Recent advances in generative modeling enable neural networks to generate weights without relying on gradient-based optimization. However, current methods are limited by issues of…
Is Meta-Learning Out? Rethinking Unsupervised Few-Shot Classification with Limited Entropy
Yunchuan Guan, Yu Liu, Ke Zhou +4
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…
Learning to Learn Weight Generation via Local Consistency Diffusion
Yunchuan Guan, Yu Liu, Ke Zhou +3
Diffusion-based algorithms have emerged as promising techniques for weight generation. However, existing solutions are limited by two challenges: generalizability and local target…
Unsupervised Meta-Learning via Dynamic Head and Heterogeneous Task Construction for Few-Shot Classification
Yunchuan Guan, Yu Liu, Ketong Liu +2
Meta-learning has been widely used in recent years in areas such as few-shot learning and reinforcement learning. However, the questions of why and when it is better than other alg…