Contrast encodes inductive bias: separating slow noise from dynamics in predictive representation learning
arXiv:2606.07770v1 Announce Type: new Abstract: Self-supervised methods that learn representations and predict dynamics fully in the latent space, such as JEPA, have been shown to confuse slowly varying noise with the dynamical signals they aim to capture. Specifically, when noise features remain approximately constant within each trajectory, contrastive predictive objectives preferentially encode these features instead of the true latent variables governing the system. The learned representation then becomes dominated by trajectory-specific noise, so downstream performance degrades with noise
This research highlights a fundamental limitation in current self-supervised predictive representation learning, particularly as more complex and real-world datasets are being utilized.
A strategic reader should care because this technical hurdle impacts the reliability and robustness of advanced AI systems, potentially limiting their applicability in critical domains if not addressed.
This research suggests a need for new architectural or objective functions in self-supervised learning to effectively separate true dynamics from noise, which could lead to more robust and accurate AI models.
- · AI researchers focused on robust representation learning
- · Ethical AI developers
- · Industries requiring high-fidelity predictive models
- · Developers solely relying on existing JEPA-like architectures
- · Applications with high noise-to-signal ratios
- · Investors in 'black box' AI solutions
Self-supervised learning models will likely undergo modifications to incorporate better noise-filtering mechanisms.
Improved representation learning could lead to more reliable autonomous systems and faster scientific discovery.
The development of more noise-resilient AI could accelerate the deployment of AI agents in complex, unstructured environments.
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Read at arXiv cs.LG