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

TL;DR

This survey reviews the progress, limitations, and future opportunities of transfer learning in computer vision, highlighting its role in improving accuracy with less data and computation.

Contribution

It provides a comprehensive overview of current transfer learning techniques in computer vision, emphasizing recent developments, challenges, and potential research directions.

Findings

Transfer learning reduces data and computation needs in CV.

Recent advancements have improved transfer learning effectiveness.

Limitations include domain adaptation and model interpretability issues.

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.