Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Self-supervised learning (SSL) methods learn features from data without manual annotations.
• Current SSL approaches, primarily contrastive learning, focus on instance-level similarity between augmented views of the same image.
• This instance-level focus may limit the scope of learned features.

Purpose of the Study:

• To explore leveraging similarity between distinct images to improve representation learning in SSL.
• To analyze the relationship between similarity loss and feature-level cross-entropy loss.
• To demonstrate that a combined loss strategy yields superior results.

Main Methods:

• Proposed a novel SSL framework that incorporates inter-image similarity.
• Analyzed the theoretical relationship between instance-level similarity loss and feature-level cross-entropy loss.
• Conducted experiments to validate the proposed method and loss combination.

Main Results:

• The proposed method effectively utilizes similarities between distinct images for richer feature learning.
• Theoretical analysis and experimental results confirm the synergy between similarity loss and cross-entropy loss.
• Achieved state-of-the-art performance, demonstrating the efficacy of the combined approach.

Conclusions:

• Leveraging inter-image similarity in SSL offers significant advantages over instance-level methods.
• The combination of similarity and cross-entropy losses is crucial for optimal performance.
• The developed approach advances representation learning in self-supervised settings.