Summary of Over-parameterized Student Model Via Tensor Decomposition Boosted Knowledge Distillation, by Yu-liang Zhan et al.
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation
by Yu-Liang Zhan, Zhong-Yi Lu, Hao Sun, Ze-Feng Gao
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 The abstract presents a novel approach to Knowledge Distillation (KD), a technique that enables compact student models to mimic larger teacher models, facilitating the transfer of knowledge from large pre-trained models. By scaling up the parameters of the student model during training and introducing a tensor decomposition strategy, the authors aim to benefit from overparameterization without increasing inference latency. The proposed approach is evaluated through comprehensive experiments in various KD tasks covering computer vision and natural language processing areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces an innovative way to make large pre-trained models more accessible by teaching smaller models to mimic them. It uses a special technique called Knowledge Distillation, which helps transfer the knowledge from big models to smaller ones. The authors developed a new method that allows these smaller models to learn quickly and accurately, even when they have many parameters. They tested their approach on several tasks in computer vision and language processing. |
Keywords
» Artificial intelligence » Inference » Knowledge distillation » Natural language processing » Student model
