Leveraging angular distributions for improved knowledge distillation

• Published in: Neurocomputing (Amsterdam) Vol. 518; pp. 466 - 481
• Main Authors: Jeon, Eun Som, Choi, Hongjun, Shukla, Ankita, Turaga, Pavan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 21-01-2023
• Subjects: Angular distribution; Angular margin; Image classification; Knowledge distillation; Angular distribution; Angular margin; Knowledge distillation; Image classification
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.11.029

Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a large capacity (teacher network) to train a smaller model (student network). Recently, additional variations for knowledge distillation, utilizing activation maps of intermediate layers as the source of knowledge, have been studied. Generally, in computer vision applications, it is seen that the feature activation learned by a higher-capacity model contains richer knowledge, highlighting complete objects while focusing less on the background. Based on this observation, we leverage the teacher’s dual ability to accurately distinguish between positive (relevant to the target object) and negative (irrelevant) areas.
We propose a new loss function for distillation, called angular margin-based distillation (AMD) loss. AMD loss uses the angular distance between positive and negative features by projecting them onto a hypersphere, motivated by the near angular distributions seen in many feature extractors. Then, we create a more attentive feature that is angularly distributed on the hypersphere by introducing an angular margin to the positive feature. Transferring such knowledge from the teacher network enables the student model to harness the teacher’s higher discrimination of positive and negative features, thus distilling superior student models. The proposed method is evaluated for various student–teacher network pairs on four public datasets. Furthermore, we show that the proposed method has advantages in compatibility with other learning techniques, such as using fine-grained features, augmentation, and other distillation methods.