Contrastive Learning Methods Struggle to Distinguish Slow Noise from True Dynamics, Study Shows
A new arXiv paper identifies a fundamental flaw in self-supervised representation learning methods like JEPA: they confuse slowly-varying noise with actual dynamical signals, degrading performance as noise increases. The issue stems from how these methods sample negative examples across different trajectories, allowing trajectory-specific noise to dominate learned representations. The findings suggest design principles for improving contrastive predictive objectives in dynamical systems, particularly for noisy experimental data.
Researchers studying self-supervised representation learning have identified a critical failure mode in popular methods like JEPA and DySIB that learn dynamics in latent space. When noise features remain approximately constant within individual trajectories, contrastive predictive objectives preferentially encode these noise features rather than the true latent variables governing the system. The problem is inherent to objectives that sample negative examples across different trajectories—a common practice in contrastive learning. The authors demonstrate this failure on synthetic moving-dot datasets and pendulum videos, showing that performance degrades with noise strength and fails to improve with more training data. They propose a remedy: sampling negative examples within single trajectories instead, which prevents slow noise from serving as a predictive shortcut. This approach forces encoders to learn genuinely relevant dynamical variables, with longer trajectories yielding better representations even under strong noise.
What's missing
The paper does not discuss computational costs or scalability implications of the proposed within-trajectory negative sampling approach compared to standard cross-trajectory sampling. Additionally, the generalizability of findings beyond the tested domains (synthetic dots, rigid-body pendulum) and applicability to real-world high-dimensional systems remains an open question.
What different sources said
- arXiv cs.LGCenter
Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning
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