PRUNE CHANNEL AND DISTILL: DISCRIMINATIVE KNOWLEDGE DISTILLATION FOR SEMANTIC SEGMENTATION

The goal of knowledge distillation (KD) for semantic segmentation is to transfer discriminative knowledge, enabling the network to distinguish pixels into each class, from a teacher to a student network. Recent KD studies for semantic segmentation fail to convey discriminative knowledge effectively to the student. Consequently, a student network with previous KD cannot generate segmentation maps that effectively distinguish the boundaries of small objects, unlike a teacher network. In this work, we propose a novel KD learning framework, prune channel and distill (PCD), which consists of channel pruning and distillation processes. To transfer the discriminative knowledge of the teacher to the student network, we propose a discriminative score from the perspective of the difference between class responses and student matching distillation, allowing the student to selectively learn channels of pruned feature maps from the teacher. Our PCD directly provides discriminative knowledge from the teacher to the student. In extensive experiments, PCD outperforms state-of-the-art methods on various semantic segmentation datasets. Representative results demonstrate that the proposed method enhances the granularity of the segmentation maps produced by the student network.