InDistill: Information flow-preserving knowledge distillation for model compression

In this paper, we introduce InDistill, a method that serves as a warmup stage for enhancing Knowledge Distillation (KD) effectiveness. InDistill focuses on transferring criti-cal information flow paths from a heavyweight teacher to a lightweight student. This is achieved via a training scheme based on curriculum learning that considers the distillation difficulty of each layer and the critical learning peri-ods when the information flow paths are established. This procedure can lead to a student model that is better pre-pared to learn from the teacher. To ensure the applicability of InDstill across a wide range of teacher-student pairs, we also incorporate a pruning operation when there is a discrepancy in the width of the teacher and student layers. This pruning operation reduces the width of the teacher's intermediate layers to match those of the student, allowing direct distillation without the need for an encoding stage. The proposed method is extensively evaluated using various pairs of teacher-student architectures on CIFAR-10, CIFAR-100, and ImageNet datasets demonstrating that preserving the information flow paths consistently increases the per-formance of the baseline KD approaches on both classi-fication and retrieval settings. The code is available at https://github.com/gsarridis/InDistill.