Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
<|reference_start|>Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities: 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.<|reference_end|>
@article{panda2024transfer,
    title={Transfer Learning Applied to Computer Vision Problems: Survey on Current
  Progress, Limitations, and Opportunities},
    author={Aaryan Panda, Damodar Panigrahi, Shaswata Mitra, Sudip Mittal, Shahram
  Rahimi},
    journal={arXiv preprint arXiv:2409.07736},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07736},
    primaryClass={cs.CV cs.AI}
}