Summary of Dual-head Knowledge Distillation: Enhancing Logits Utilization with An Auxiliary Head, by Penghui Yang et al.
Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head
by Penghui Yang, Chen-Chen Zong, Sheng-Jun Huang, Lei Feng, Bo An
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore a novel approach to knowledge distillation by introducing a logit-level loss function in addition to the traditional probability-level loss function. The goal is to leverage the latent information present in logits, but the authors find that combining both losses leads to performance degradation. They attribute this issue to the collapse of the classification head and propose a new method called dual-head knowledge distillation, which partitions the linear classifier into two heads responsible for different losses. Experimental results show that this approach can effectively utilize the information inside logits and outperform state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to help machines learn from each other. They tried combining two types of loss functions to make it work, but it actually made things worse. So, they came up with a new idea called dual-head knowledge distillation. It’s like having two separate brains that work together to help the machine learn more effectively. The results show that this approach is better than what was being used before. |
Keywords
» Artificial intelligence » Classification » Knowledge distillation » Logits » Loss function » Probability
