SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning
This work addresses a specific bottleneck in meta-learning for few-shot learning, offering an incremental improvement with broad applicability across GBML methods.
The paper tackles the problem of gradient-based meta-learning (GBML) by hypothesizing that it implicitly suppresses the Hessian along the optimization trajectory, and introduces SHOT, an algorithm that minimizes parameter distance to suppress the Hessian, which outperforms baselines in few-shot learning tasks.
In this paper, we hypothesize that gradient-based meta-learning (GBML) implicitly suppresses the Hessian along the optimization trajectory in the inner loop. Based on this hypothesis, we introduce an algorithm called SHOT (Suppressing the Hessian along the Optimization Trajectory) that minimizes the distance between the parameters of the target and reference models to suppress the Hessian in the inner loop. Despite dealing with high-order terms, SHOT does not increase the computational complexity of the baseline model much. It is agnostic to both the algorithm and architecture used in GBML, making it highly versatile and applicable to any GBML baseline. To validate the effectiveness of SHOT, we conduct empirical tests on standard few-shot learning tasks and qualitatively analyze its dynamics. We confirm our hypothesis empirically and demonstrate that SHOT outperforms the corresponding baseline. Code is available at: https://github.com/JunHoo-Lee/SHOT