Summary of Zero-shot Active Learning Using Self Supervised Learning, by Abhishek Sinha et al.
Zero-shot Active Learning Using Self Supervised Learning
by Abhishek Sinha, Shreya Singh
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 active learning approach that leverages self-supervised learned features to select the most informative unlabelled data for annotation, given a fixed budget. The method aims to maximize the generalization performance of deep learning models while minimizing annotation costs. By utilizing self-supervised learning, the approach can obtain useful feature representations without requiring annotations. This model-agnostic and non-iterative approach has the potential to improve the efficiency of active learning in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making it easier to train deep learning models. Right now, we need a lot of labeled data to make these models work well. But getting that labeled data is hard and expensive. A way to make it easier is called Active Learning. It helps us choose the most important unlabeled data to label first. This makes our model better at making predictions without needing as much labeled data. In this paper, we’re proposing a new way of doing Active Learning that doesn’t require an iterative process and works with any deep learning model. We’re using self-supervised learning to get good features from the data without needing labels. This could make it easier and cheaper to train our models. |
Keywords
* Artificial intelligence * Active learning * Deep learning * Generalization * Self supervised
