Summary of M2mkd: Module-to-module Knowledge Distillation For Modular Transformers, by Ka Man Lo et al.
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers
by Ka Man Lo, Yiming Liang, Wenyu Du, Yuantao Fan, Zili Wang, Wenhao Huang, Lei Ma, Jie Fu
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to train modular neural architectures, which are designed for efficient adaptation and generalization. Modular models consist of intrinsic sparse connectivity, making training challenging due to optimization difficulties. To address this issue, the authors develop module-to-module knowledge distillation (m2mKD), a technique that transfers knowledge between modules using a shared meta model. m2mKD combines teacher modules from a pretrained monolithic model with student modules from a modular model. The authors evaluate m2mKD on two modular architectures: Neural Attentive Circuits (NACs) and Vision Mixture-of-Experts (V-MoE). Results show significant improvements in IID accuracy (up to 5.6%) and OOD robustness (up to 4.2%) for NACs, as well as higher accuracy (3.5%) on ImageNet-1k when training V-MoE-Base with m2mKD. The authors release code at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn faster and better by sharing knowledge between different parts of their brains. Computers have a hard time learning new things because they don’t understand how to use the information they already know. To fix this, scientists created a new way to teach computers called module-to-module knowledge distillation (m2mKD). m2mKD takes information from one part of the computer’s brain and teaches it to another part. The scientists tested this method on two types of computer brains: Neural Attentive Circuits and Vision Mixture-of-Experts. They found that m2mKD helped these computers learn faster and better, especially when dealing with new or unusual situations. |
Keywords
* Artificial intelligence * Generalization * Knowledge distillation * Mixture of experts * Optimization
