Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation
arXiv:2509.24467v3 Announce Type: replace Abstract: Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanations. We introduce KREPES, a unified framework to analytically interpret the learned representations of SSL objectives, including SimCLR, BYOL, and VICReg. By bridging empirical neural tangent kernel approximations of neural networks with the Representer Theorem for kernels, we express the learned latent space directly via "Representer Landmarks", which are the repr
The increasing complexity and opacity of self-supervised learning models necessitate new interpretability techniques to foster trust and accelerate deployment.
Improved interpretability of AI models is crucial for regulatory compliance, debugging, and broader adoption in critical applications, moving AI beyond 'black box' operations.
This research provides a framework for understanding the internal workings of complex self-supervised models, potentially transforming how they are designed, validated, and deployed.
- · AI developers
- · AI regulators
- · Industries adopting advanced AI
- · Academic researchers
- · Organizations reliant on opaque AI systems
More transparent and trustworthy AI systems will become easier to develop and deploy.
This can accelerate the adoption of advanced AI in regulated and high-stakes environments, such as healthcare and finance.
Increased public and institutional trust in AI could lead to faster societal integration of highly autonomous systems.
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Read at arXiv cs.LG