Summary of Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities, by Aaryan Panda et al.
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
by Aaryan Panda, Damodar Panigrahi, Shaswata Mitra, Sudip Mittal, Shahram Rahimi
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed paper explores the evolution of Computer Vision (CV) by examining the impact of machine learning (ML), particularly Transfer Learning (TL). Initially, CV relied on handcrafted features and rule-based algorithms, but the introduction of ML has brought significant advancements. TL enables the reuse of pre-trained models for various CV tasks, requiring less data and computing while achieving nearly equal accuracy. This paper discusses recent developments, limitations, and opportunities in TL development and its applications in solving real-world problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how machine learning helps computer vision solve everyday problems. Computer vision started by relying on manual features and rules, but now it uses machine learning to make progress. One key technique is called transfer learning, which takes pre-trained models and adapts them for different tasks. This makes it possible to achieve good results with less data and computing power. The paper looks at the current state of this technology, its limitations, and where it can go in the future. |
Keywords
» Artificial intelligence » Machine learning » Transfer learning
