Summary of Connect Later: Improving Fine-tuning For Robustness with Targeted Augmentations, by Helen Qu et al.
Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations
by Helen Qu, Sang Michael Xie
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 investigates the challenges of deploying machine learning models trained on labeled data to new, unseen environments. Specifically, it focuses on the problem of domain adaptation, where a model trained on one type of data (e.g., images from wildlife camera traps) struggles to generalize to another type of data (e.g., images from new camera trap locations). The authors propose a novel approach called Connect Later, which involves pre-training a model using generic augmentations and then fine-tuning it with targeted augmentations designed for the specific domain shift. The results show that this approach outperforms standard fine-tuning and supervised learning on several real-world datasets, including astronomical time-series classification, wildlife species identification, tumor identification, and redshift prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about teaching machines to adapt to new situations. When a machine learns from one set of data, it often struggles to understand another type of data that’s slightly different. The authors suggest a way to make the machine learn better by giving it more information about what to look for in the new situation. This helps the machine generalize better and makes it more useful in real-world applications. |
Keywords
* Artificial intelligence * Classification * Domain adaptation * Fine tuning * Machine learning * Supervised * Time series
