Distillation embedded absorbable pruning for fast object re-identification

Abstract

Combining knowledge distillation (KD) and network pruning (NP) shows promise in learning a light network to accelerate object re-identification. However, KD requires an untrained student network to establish more critical connections in early epochs, but NP demands a well-trained student network to avoid destroying critical connections. This presents a dilemma, potentially leading to a collapse of the student network and harming object Re-ID performance. For that, we propose a distillation embedded absorbable pruning (DEAP) method. We design a pruner-convolution-pruner (PCP) unit to resolve the dilemma by loading NP’s sparse regularization on extra untrained pruners. Additionally, we propose an asymmetric relation knowledge distillation method. It readily transfers feature representation knowledge and asymmetric pairwise similarity knowledge without using additional adaptation modules. Finally, we apply re-parameterization to absorb pruners of PCP units to simplify student networks. Experiments demonstrate the superiority of DEAP, such as on the VeRi-776 dataset, with ResNet-101 as a teacher, DEAP saves 73.24% of model parameters and 71.98% of floating-point operations without sacrificing accuracy.