Summary of Lakd-activation Mapping Distillation Based on Local Learning, by Yaoze Zhang and Yuming Zhang and Yu Zhao and Yue Zhang and Feiyu Zhu
LAKD-Activation Mapping Distillation Based on Local Learning
by Yaoze Zhang, Yuming Zhang, Yu Zhao, Yue Zhang, Feiyu Zhu
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The novel Local Attention Knowledge Distillation (LAKD) framework efficiently utilizes distilled information from teacher networks to achieve higher interpretability and competitive performance. Existing knowledge distillation methods focus on designing different distillation targets but overlook efficient utilization of distilled information, crudely coupling different types of information. LAKD establishes an independent interactive training mechanism through a separation-decoupling mechanism and non-directional activation mapping. The student network is divided into local modules with independent gradients to decouple the knowledge transferred from the teacher. Non-directional activation mapping helps integrate knowledge from different local modules by learning coarse-grained feature knowledge. The framework demonstrates state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make AI models smarter by sharing knowledge between them. This is called “knowledge distillation.” Most current methods focus on making the smaller model learn from the bigger one, but they don’t think about how to use this shared knowledge well. The authors developed a new method called Local Attention Knowledge Distillation (LAKD) that allows the smaller model to learn more effectively by breaking down complex information into simpler parts and then combining it in a way that makes sense. |
Keywords
» Artificial intelligence » Attention » Distillation » Knowledge distillation
