SHOT: Suppressing the Hessian along the Optimization Trajectory for Gradient-Based Meta-Learning
Main Conference Track
Authors: JunHoo Lee, Jayeon Yoo, Nojun Kwak

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.