Summary of A Cross-domain Benchmark For Active Learning, by Thorben Werner et al.
A Cross-Domain Benchmark for Active Learning
by Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme
First submitted to arxiv on: 1 Aug 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 The proposed CDALBench is an active learning benchmark that aims to overcome limitations in current AL research by providing tasks across computer vision, natural language processing, and tabular learning. The benchmark includes an efficient greedy oracle, allowing for 50 runs per experiment. This enables sophisticated evaluation of AL methods and highlights the importance of cross-domain benchmarks and multiple repetitions. The study demonstrates that method superiority varies across domains, emphasizing the need for comprehensive evaluations. Additionally, it shows that a large number of runs is crucial to ensure accurate performance assessments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Active learning (AL) helps reduce data labeling costs by identifying informative samples. However, current research lacks robust evaluation methods, which can lead to poor generalization and limited experimentation. The CDALBench addresses these issues by offering tasks in computer vision, natural language processing, and tabular learning, as well as an efficient oracle for 50 runs per experiment. This allows researchers to thoroughly evaluate AL methods and understand their strengths and weaknesses across different domains. |
Keywords
* Artificial intelligence * Active learning * Data labeling * Generalization * Natural language processing
