PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment
Non-contrastive self-supervised learning (SSL) is an effective framework for predictive representation learning, but popular (and in practice effective) methods such as SimSiam, BYOL, I-JEPA or DINO, which rely on a form of self-distillation to train a teacher-student network, remain poorly understood as they typically do not minimize a well-defined objective. We analyze the dynamics of a variant of the Joint Embedding Predictive Architecture (JEPA) using a regularized linear regressor to predict the learned representations of two views of the data from one another, and fully characterize its stability: non-collapsed stable equilibria align with leading nonlinear canonical correlation subspaces, while collapsed equilibria may also be stable attractors. Motivated by this result, we introduce PEIRA, a non-contrastive SSL method with an explicit objective defined through the trace of the optimal linear regressor. We show that its only stable equilibria are nontrivial global minimizers and recover the same canonical correlation subspaces, with regularization selecting the effective dimension. Experiments on ImageNet-1K and CIFAR-10 show PEIRA is competitive with VICReg and LeJEPA baselines, and qualitative empirical results support the theory.
Code (0)
등록된 구현이 없습니다.
Tasks
Self-Supervised LearningRepresentation LearningSimilar Papers 제목 키워드 기반
Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new type…
Predictive Coding, Variational Autoencoders, and Biological Connections
This paper reviews predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas. As each area is pr…
BIG-bench Machine LearningSiamJEPA: On the Role of Siamese Student Encoders in JEPA
Recently, Joint Embedding Predictive Architectures (JEPAs) have attracted significant attention in the computer vision and machine learning communities as a promising framework for self-supervised representation learning…
Representation LearningUnderstanding Adversarial Attacks on Autoencoders
Adversarial vulnerability is a fundamental limitation of deep neural networks which remains poorly understood. Recent work suggests that adversarial attacks on deep neural network classifiers exploit the …
Compressive SensingKnowledge DistillationA Geometric View of Counterfactual Behavior: Interaction of Boundary Proximity and Local Support
Counterfactual explanations seek small, semantically meaningful changes to an input that alter a model's prediction, and are widely used to interpret and audit machine learning systems. In modern vision, language, and mu…