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Related Experiment Video

Updated: Aug 29, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Self-supervised knowledge distillation for complementary label learning.

Jiabin Liu¹, Biao Li², Minglong Lei³

¹School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.

Neural Networks : the Official Journal of the International Neural Network Society

|September 13, 2022

Summary

This summary is machine-generated.

This study introduces a new machine learning approach using complementary labels, which are more efficient to collect than traditional accuracy labels. The proposed method enhances complementary label learning by integrating self-supervised learning and self-distillation techniques.

Keywords:

Complementary labels learning Knowledge distillation Self-supervision learning

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Last Updated: Aug 29, 2025

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Area of Science:

Machine Learning
Computer Vision

Background:

Traditional supervised learning requires accurate labels, which are costly and time-consuming to obtain.
Complementary labels, indicating incorrect classes, offer a more efficient alternative for data labeling.
Existing methods for complementary label learning often neglect valuable information within the data and models themselves.

Purpose of the Study:

To propose a novel framework for complementary label learning that leverages self-supervised learning and self-distillation.
To improve the performance of models trained with complementary labels by utilizing inherent data and model information.
To address the limitations of current state-of-the-art methods in complementary label learning.

Main Methods:

Integration of self-supervised learning (rotation and transformation) as an auxiliary task to learn transferable representations.
Application of entropy regularization to ensure sharper network outputs.
Utilization of knowledge distillation to transfer "dark knowledge" from a teacher network to a student network.
Development of a unified framework combining these techniques for complementary learning.

Main Results:

The proposed method demonstrates significant improvements in accuracy compared to existing state-of-the-art approaches.
Experiments show the effectiveness of integrating self-supervised learning and self-distillation in complementary label learning.
The framework successfully extracts and utilizes rich information from data and models for enhanced performance.

Conclusions:

The novel framework effectively enhances complementary label learning by incorporating self-supervised and self-distillation strategies.
This approach offers a more efficient and powerful alternative to traditional supervised learning paradigms.
The findings highlight the potential of leveraging data and model introspection for improved machine learning performance.