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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Semi-Supervised and Unsupervised Deep Visual Learning: A Survey.

Yanbei Chen, Massimiliano Mancini, Xiatian Zhu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 25, 2022

Summary

This summary is machine-generated.

This survey explores advanced deep learning methods for visual recognition using semi-supervised learning (SSL) and unsupervised learning (UL). These techniques leverage unlabeled data to improve model generalization, reducing reliance on costly manual annotations.

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

State-of-the-art deep learning models require extensive labeled data, which is costly and can hinder generalizability.
Semi-supervised learning (SSL) and unsupervised learning (UL) offer alternative approaches to utilize abundant unlabeled visual data.

Purpose of the Study:

To provide a unified review of recent advanced deep learning algorithms in SSL and UL for visual recognition.
To propose a taxonomy for categorizing and analyzing existing SSL and UL methods.
To highlight design rationales and applications across various learning scenarios and computer vision tasks.

Main Methods:

A comprehensive survey of recent deep learning algorithms in SSL and UL.
Development of a unified taxonomy to categorize representative SSL and UL methods.
Analysis of algorithms based on learning scenarios and computer vision applications.

Main Results:

Recent progress in SSL and UL demonstrates significant benefits in improving model generalization and initialization.
A unified perspective and taxonomy are presented for a holistic understanding of the field.
Categorization highlights design rationales and applications in diverse visual recognition tasks.

Conclusions:

SSL and UL are crucial for overcoming the limitations of labeled data in deep learning for visual recognition.
The proposed taxonomy offers a structured overview of current SSL and UL techniques.
Future research should focus on emerging trends and open challenges in these learning paradigms.