Core Concepts
LumiNet introduces a novel approach to knowledge distillation by enhancing logit-based distillation with the concept of 'perception', addressing overconfidence issues and improving knowledge extraction.
Abstract
LumiNet introduces a novel knowledge distillation algorithm called LumiNet.
The paper discusses the challenges associated with logit-based and feature-based knowledge distillation.
LumiNet aims to enhance logit-based distillation by introducing the concept of 'perception'.
The method focuses on calibrating logits based on the model's representation capability to address overconfidence issues.
LumiNet outperforms leading feature-based methods on benchmarks like CIFAR-100, ImageNet, and MSCOCO.
The paper includes experiments on image recognition, object detection, and transfer learning tasks.
LumiNet showcases superior performance and efficiency compared to traditional knowledge distillation methods.
Stats
LumiNet는 CIFAR-100, ImageNet 및 MSCOCO와 같은 벤치마크에서 선도적인 feature-based 방법을 능가합니다.
ResNet18 및 MobileNetV2와 비교하여 ImageNet에서 1.5% 및 2.05%의 개선을 보여줍니다.
Quotes
"LumiNet excels on benchmarks like CIFAR-100, ImageNet, and MSCOCO, outperforming leading feature-based methods."
"LumiNet introduces a novel approach to knowledge distillation by enhancing logit-based distillation with the concept of 'perception'."