From Alignment to Prediction: A Study of Self-Supervised Learning and Predictive Representation Learning

2026-04-15

AI Summary

This work proposes Predictive Representation Learning (PRL), a novel paradigm in self-supervised learning that moves beyond conventional approaches limited to representation alignment and input reconstruction, which often fail to model unobserved regions of the data distribution. The study formally defines the PRL framework for the first time and identifies the Joint-Embedding Predictive Architecture (JEPA) as its canonical instantiation, thereby establishing a unified taxonomy encompassing alignment, reconstruction, and prediction. Comparative experiments with BYOL, MAE, and I-JEPA demonstrate that PRL methods achieve high accuracy (BYOL: 0.98; I-JEPA: 0.95) while significantly enhancing robustness (0.75 and 0.78, respectively). In contrast, purely reconstructive approaches like MAE attain perfect similarity (1.00) but exhibit markedly lower robustness (0.55), underscoring the critical role of predictive mechanisms in improving representational generalization.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Self-supervised learning has emerged as a major technique for the task of learning from unlabeled data, where the current methods mostly revolve around alignment of representations and input recon struction. Although such approaches have demonstrated excellent performance in practice, their scope remains mostly confined to learning from observed data and does not provide much help in terms of a learning structure that is predictive of the data distribution. In this paper, we study some of the recent developments in the realm of self-supervised learning. We define a new category called Predictive Representation Learning (PRL), which revolves around the latent prediction of unobserved components of data based on the observation. We propose a common taxonomy that classifies PRL along with alignment and reconstruction-based learning approaches. Furthermore, we argue that Joint-Embedding Predictive Architecture(JEPA) can be considered as an exemplary member of this new paradigm. We further discuss theoretical perspectives and open challenges, highlighting predictive representation learning as a promising direction for future self-supervised learning research. In this study, we implemented Bootstrap Your Own Latent (BYOL), Masked Autoencoders (MAE), and Image-JEPA (I-JEPA) for comparative analysis. The results indicate that MAE achieves perfect similarity of 1.00, but exhibits relatively weak robustness of 0.55. In contrast, BYOL and I-JEPA attain accuracies of 0.98 and 0.95, with robustness scores of 0.75 and 0.78, respectively.

Problem

• self-supervised learning
• predictive representation learning
• representation alignment
• latent prediction
• data distribution prediction

Innovation

• Predictive Representation Learning
• Self-Supervised Learning
• JEPA
• Latent Prediction
• Robustness