Structural Knowledge Distillation for Object Detection
This work addresses the problem of inefficient knowledge transfer in object detection for practitioners seeking lightweight models, though it is incremental as it builds on existing KD techniques.
The paper tackles improving knowledge distillation for object detection by replacing pixel-wise lp-norm losses with a structural similarity (SSIM)-based loss that incorporates contrast and structural cues, resulting in a +3.5 AP gain on MSCOCO with Faster R-CNN R-50 compared to vanilla models and outperforming more complex state-of-the-art methods.
Knowledge Distillation (KD) is a well-known training paradigm in deep neural networks where knowledge acquired by a large teacher model is transferred to a small student. KD has proven to be an effective technique to significantly improve the student's performance for various tasks including object detection. As such, KD techniques mostly rely on guidance at the intermediate feature level, which is typically implemented by minimizing an lp-norm distance between teacher and student activations during training. In this paper, we propose a replacement for the pixel-wise independent lp-norm based on the structural similarity (SSIM). By taking into account additional contrast and structural cues, feature importance, correlation and spatial dependence in the feature space are considered in the loss formulation. Extensive experiments on MSCOCO demonstrate the effectiveness of our method across different training schemes and architectures. Our method adds only little computational overhead, is straightforward to implement and at the same time it significantly outperforms the standard lp-norms. Moreover, more complex state-of-the-art KD methods using attention-based sampling mechanisms are outperformed, including a +3.5 AP gain using a Faster R-CNN R-50 compared to a vanilla model.