Self-supervised learning detects hidden signals despite distractions
Predictive Self-Supervised Learning Provably Identifies Stochastic Signals under Nuisance
Machine LearningArtificial Intelligence
Summary
Predicting future information without reconstructing raw data helps machines learn important features. The authors show that this learning method can distinguish meaningful hidden signals from irrelevant noise, even when both cause unpredictability. They prove mathematically why this works and confirm it on simulated data with noisy, changing environments. This finding explains how self-supervised learning can focus on useful information automatically.
What this means in practice
- •For machine learning engineers: Design self-supervised systems that robustly extract useful latent signals despite noisy, irrelevant inputs.
- •For signal processing teams: Distinguish relevant stochastic patterns from nuisance variables in complex dynamic data scenarios.
Tested on simulated data.
Authors
Fabian A. Mikulasch, Friedemann Zenke
Abstract
Self-supervised learning (SSL) by predicting in latent space, without generating the input data itself, learns highly abstract, useful representations. Intuitively, this success is often attributed to its ability to discard nuisance information that is irrelevant to prediction. However, this poses a conundrum: both stochastic variation in a prediction-relevant latent signal and true nuisance make observations partly unpredictable; how could they be distinguished? Surprisingly, we prove that common SSL methods can achieve exactly this, by implicitly instantiating a latent-variable model with stochastic dynamics and observation-private nuisance. We trace their ability to recover the stochastic signal to two complementary principles: Predictive mutual information maximization ensures that representations retain the information needed for prediction, while latent distribution matching constrains how this information is encoded, thereby making the retained signal identifiable. We confirm this identifiability result in simulations for Gaussian predictors, which recover the true signal up to an affine transformation even in dynamic, nuisance-laden environments.