Work

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

Year: 2024

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2409.07736

Authors: Panda, Aaryan, Panigrahi, Damodar, Mitra, Shaswata, Mittal, Sudip, Rahimi, Shahram

Institutions: Hillsborough County Public Schools, Mississippi State University

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4403663217

Cites:

Cited by: 3

Related to: 10