Summary of Bissl: a Bilevel Optimization Framework For Enhancing the Alignment Between Self-supervised Pre-training and Downstream Fine-tuning, by Gustav Wagner Zakarias et al.
BiSSL: A Bilevel Optimization Framework for Enhancing the Alignment Between Self-Supervised Pre-Training and Downstream Fine-Tuning
by Gustav Wagner Zakarias, Lars Kai Hansen, Zheng-Hua Tan
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 study introduces BiSSL, a novel training framework that optimizes self-supervised learning by aligning pretext pre-training and downstream fine-tuning stages. BiSSL uses bilevel optimization to enhance information sharing between these stages, resulting in better-initialized backbone parameters for the downstream task. The proposed algorithm alternates between optimizing objectives for pretext pre-training and downstream tasks, applicable to various pretext and downstream tasks. Evaluations on 12 image classification datasets and object detection show significant performance gains using BiSSL-trained ResNet-50 backbones on ImageNet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiSSL is a new way to train AI models that helps them learn better from examples alone. It’s like a game where the model plays with itself before being trained on real tasks. This makes the model’s early learning more useful for later tasks. The paper shows that using BiSSL improves how well AI models do on many different image recognition and object detection tasks. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Object detection » Optimization » Resnet » Self supervised
