ScholarWorks@성균관대학교

초록

The ability to rapidly adapt to unseen tasks is a fundamental objective in few-shot learning. Recent advances in optimization-based meta-learning have enhanced adaptability by learning sharable prior knowledge across tasks with just a few gradient descent steps. However, we argue that this shared prior knowledge can exert an imbalanced influence on individual samples within tasks, potentially resulting in a broad loss distribution where samples closely aligned with the prior knowledge exhibit low loss values, while others display high loss values. Furthermore, our experiments show that gradients computed as the average from a broad loss distribution tend to be non-representative and low, leading to poor generalization performance since the contribution of high-loss samples is diminished by low-loss samples. To address this, we propose a novel meta-learning method that arbitrates gradient norms based on sample-aware information during task adaptation. Specifically, we first normalize the gradient vector to reduce the imbalanced influence of prior knowledge on individual samples. Subsequently, the Arbiter, a learnable network, dynamically scales the current gradient norm by analyzing the relationship between original gradient norms and weight norms, which indicates the model's sensitivity and complexity to each sample. In this way, the proposed method, Meta-learning with Gradient Norm Arbitration (Meta-GNA), improves generalization performance by preserving more representative and higher gradients that adequately reflect high-loss samples, which are distantly aligned with prior knowledge. Experimental results show that Meta-GNA improves performance in few-shot classification, particularly in cross-domain scenarios where the imbalance in prior knowledge across samples is more pronounced.

키워드