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🧠 AI⚪ NeutralImportance 7/10
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
arXiv – CS AI|Jonas Ngnaw\'e, Maxime Heuillet, Sabyasachi Sahoo, Yann Pequignot, Ola Ahmad, Audrey Durand, Fr\'ed\'eric Precioso, Christian Gagn\'e||5 views
🤖AI Summary
Researchers identified that fine-tuning non-robust pretrained AI models with robust objectives can lead to poor performance, termed 'suboptimal transfer.' They propose Epsilon-Scheduling, a novel training technique that adjusts perturbation strength during training to improve both task adaptation and adversarial robustness.
Key Takeaways
- →Fine-tuning non-robust pretrained models with robust objectives often leads to suboptimal transfer and poor performance.
- →The researchers introduced Epsilon-Scheduling, a training heuristic that schedules perturbation strength to promote optimal transfer.
- →A new metric called 'expected robustness' was developed to better evaluate the accuracy-robustness trade-off across different models.
- →Extensive experiments across six pretrained models and five datasets showed consistent improvements in expected robustness.
- →The findings address a significant knowledge gap in robust fine-tuning from widely available non-robust pretrained models.
#machine-learning#fine-tuning#adversarial-robustness#pretrained-models#epsilon-scheduling#transfer-learning#ai-research#model-training
Read Original →via arXiv – CS AI
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