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arXiv · 2409.07736

Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities

Aaryan Panda·Damodar Panigrahi·Shaswata Mitra·Sudip Mittal·Shahram Rahimi
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Abstract

The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.

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Aaryan Panda, Damodar Panigrahi, Shaswata Mitra, Sudip Mittal, Shahram Rahimi. 2024-09-12. Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities. https://arxiv.org/abs/2409.07736

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