Summary of Distilling Invariant Representations with Dual Augmentation, by Nikolaos Giakoumoglou et al.
Distilling Invariant Representations with Dual Augmentation
by Nikolaos Giakoumoglou, Tania Stathaki
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a novel knowledge distillation (KD) method to improve the robustness and transferability of student models by introducing a dual augmentation strategy. Building upon recent methods that incorporate causal interpretations, our approach applies different augmentations to both teacher and student models during distillation, encouraging the student to learn invariant features. This strategy complements invariant causal distillation by ensuring learned representations remain stable across various data variations. The proposed method is evaluated on CIFAR-100, achieving competitive results in same-architecture KD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making small AI models that can work well with big ones. It’s a way to transfer knowledge from powerful models to smaller ones so they can do tasks better too. The idea is to make the small model learn features that stay the same even when the data changes. To achieve this, the authors propose a new method that uses different ways of changing the data for both the big and small models during training. This helps the small model learn features that work well with many types of data. The results show that this approach is effective and can be used to improve AI models. |
Keywords
» Artificial intelligence » Distillation » Knowledge distillation » Transferability
