LumiNet: The Bright Side of Perceptual Knowledge Distillation

• URL: http://arxiv.org/abs/2310.03669v2
• Date: Sat, 9 Mar 2024 07:15:24 GMT
• Title: LumiNet: The Bright Side of Perceptual Knowledge Distillation
• Authors: Md. Ismail Hossain, M M Lutfe Elahi, Sameera Ramasinghe, Ali Cheraghian, Fuad Rahman, Nabeel Mohammed, Shafin Rahman
• Abstract summary: We present LumiNet, a novel knowledge distillation algorithm designed to enhance logit-based distillation. LumiNet addresses overconfidence issues in logit-based distillation method while also introducing a novel method to distill knowledge from the teacher. It excels on benchmarks like CIFAR-100, ImageNet, and MSCOCO, outperforming leading feature-based methods.
• Score: 18.126581058419713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models. In contrast, logit-based approaches, which aim to distill `dark knowledge' from teachers, typically exhibit inferior performance compared to feature-based methods. To bridge this gap, we present LumiNet, a novel knowledge distillation algorithm designed to enhance logit-based distillation. We introduce the concept of 'perception', aiming to calibrate logits based on the model's representation capability. This concept addresses overconfidence issues in logit-based distillation method while also introducing a novel method to distill knowledge from the teacher. It reconstructs the logits of a sample/instances by considering relationships with other samples in the batch. LumiNet excels on benchmarks like CIFAR-100, ImageNet, and MSCOCO, outperforming leading feature-based methods, e.g., compared to KD with ResNet18 and MobileNetV2 on ImageNet, it shows improvements of 1.5% and 2.05%, respectively.

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