Summary of Towards Effective Data-free Knowledge Distillation Via Diverse Diffusion Augmentation, by Muquan Li et al.
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion Augmentation
by Muquan Li, Dongyang Zhang, Tao He, Xiurui Xie, Yuan-Fang Li, Ke Qin
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces an innovative approach to data-free knowledge distillation (DFKD) called diverse diffusion augmentation (DDA). The traditional DFKD methods that use synthesized training data are limited by the lack of diversity and distribution discrepancies between the synthesized and original datasets. To overcome these challenges, the authors propose a composite process that combines data synthesis with subsequent diffusion models for self-supervised augmentation. This approach generates a range of data samples with similar distributions while retaining controlled variations. Additionally, an image filtering technique based on cosine similarity is introduced to maintain fidelity during the knowledge distillation process. Experimental results on CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets demonstrate the superior performance of DDA-based DFKD compared to contemporary state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new way to help machines learn from each other without needing lots of data. Right now, this type of learning requires fake data to be created, but it’s not very good at it. The authors have come up with a clever trick to make the process better. They combine two things: making fake data and using special computer models that can create new versions of that data. This makes the fake data more like real data, which helps the machines learn better. The authors tested this method on some pictures and it worked really well! |
Keywords
» Artificial intelligence » Cosine similarity » Diffusion » Knowledge distillation » Self supervised
