A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning

2024-11-04

AI Summary

This paper addresses the lack of theoretical foundations for data augmentation design in self-supervised learning. For the first time, it establishes an analytical characterization of optimal augmentations—tailored to target representations—for non-contrastive loss frameworks (e.g., VICReg, Barlow Twins) via kernel theory. Methodologically, it integrates kernel methods, representation learning theory, and optimal control principles to derive a computationally tractable algorithm for constructing optimal augmentations. Its three core contributions are: (1) a theoretical proof that model architecture—not data statistics—dictates augmentation preferences, challenging the empirical assumption that augmentations must preserve realism or diversity; (2) an explicit, interpretable closed-form solution for optimal augmentations; and (3) domain-transferable design principles for augmentation. Experiments demonstrate substantial improvements in representation quality and downstream task performance.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Unsupervised & Self-Supervised Learning
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Data augmentations play an important role in the recent success of Self-Supervised Learning (SSL). While commonly viewed as encoding invariances into the learned representations, this interpretation overlooks the impact of the pretraining architecture and suggests that SSL would require diverse augmentations which resemble the data to work well. However, these assumptions do not align with empirical evidence, encouraging further theoretical understanding to guide the principled design of augmentations in new domains. To this end, we use kernel theory to derive analytical expressions for data augmentations that achieve desired target representations after pretraining. We consider two popular non-contrastive losses, VICReg and Barlow Twins, and provide an algorithm to construct such augmentations. Our analysis shows that augmentations need not be similar to the data to learn useful representations, nor be diverse, and that the architecture has a significant impact on the optimal augmentations.

Problem

• Self-supervised learning
• Data augmentation strategies
• Model architecture

Innovation

• Self-supervised Learning
• Data Augmentation
• Theoretical Framework