Summary of Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks, by Shin’ya Yamaguchi et al.
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
by Shin’ya Yamaguchi, Sekitoshi Kanai, Kazuki Adachi, Daiki Chijiwa
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed adaptive random feature regularization (AdaRand) method improves upon traditional fine-tuning techniques by adapting the distribution of feature vectors for downstream classification tasks without requiring additional source information or significant computational overhead. By minimizing the gap between feature vectors and randomly sampled reference vectors, AdaRand dynamically updates the conditional distribution to balance class distances in feature spaces, outperforming other regularization methods that rely on auxiliary sources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method called adaptive random feature regularization (AdaRand) helps deep neural networks adapt to small target datasets without extra information or heavy computations. It works by matching feature vectors with randomly generated reference points and updating the distribution of features based on the current model. This makes AdaRand better than other methods that need more data or lots of calculations. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Regularization
