Toward Effective Knowledge Distillation: Navigating Beyond Small-data Pitfall.

Abstract

The spectacular success of training large models on extensive datasets highlights the potential of scaling up for exceptional performance. To deploy these models on edge devices, knowledge distillation (KD) is commonly used to create a compact model from a larger, pretrained teacher model. However, as models and datasets rapidly scale up in practical applications, it is crucial to consider the applicability of existing KD approaches originally designed for limited-capacity architectures and small-scale datasets. In this paper, we revisit current KD methods and identify the presence of a small-data pitfall, where most modifications to vanilla KD prove ineffective on large-scale datasets. To guide the design of consistently effective KD methods across different data scales, we conduct a meticulous evaluation of the knowledge transfer process. Our findings reveal that incorporating more useful information is crucial for achieving consistently effective KD methods, while modifications in loss functions show relatively less significance. In light of this, we present a paradigmatic example that combines vanilla KD with deep supervision, incorporating additional information into the student during distillation. This approach surpasses almost all recent KD methods. We believe our study will offer valuable insights to guide the community in navigating beyond the small-data pitfall and toward consistently effective KD.