Summary of Fast Fishing: Approximating Bait For Efficient and Scalable Deep Active Image Classification, by Denis Huseljic and Paul Hahn and Marek Herde and Lukas Rauch and Bernhard Sick
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification
by Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch, Bernhard Sick
First submitted to arxiv on: 13 Apr 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 introduces two methods to enhance the computational efficiency and scalability of BAIT, a deep active learning strategy that has shown impressive performance across various datasets. The authors significantly reduce BAIT’s time complexity by approximating the Fisher Information, making it possible to use on large-scale classification tasks such as ImageNet. They achieve strong performance with considerably reduced time complexity through two adaptations: taking the expectation over the most probable classes and constructing a binary classification task. The paper provides an open-source toolbox that implements recent state-of-the-art AL strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes deep active learning (AL) more accessible by making it faster and more efficient. Currently, some AL methods are too slow or use too much memory to be used on large datasets. The authors create two new ways to make BAIT, a popular AL strategy, work better with big datasets like ImageNet. They do this by simplifying the math behind BAIT, which makes it run faster and uses less memory. This means that researchers can now use BAIT on many more types of problems. |
Keywords
» Artificial intelligence » Active learning » Classification
