UR-JEPA: Uniform Rectifiability as a Regularizer for Joint-Embedding Predictive Architectures
arXiv:2606.01443v1 Announce Type: new Abstract: A central difficulty in training Joint-Embedding Predictive Architectures (JEPAs) is preventing representation collapse. LeJEPA addresses this by enforcing an isotropic Gaussian target on the embeddings via Sketched Isotropic Gaussian Regularization (SIGReg). This target is in tension with the manifold hypothesis, which expects embeddings to concentrate on a low-dimensional subset of the ambient space. We propose \emph{UR-JEPA}, which targets a uniformly $n$-rectifiable measure of local tangent dimension $n$ at small scales, realized through a Ga
This research addresses a fundamental challenge in current AI model training (representation collapse in JEPAs), an increasingly critical issue as AI systems become more complex and data-intensive.
Improving the efficiency and stability of large-scale AI model training directly impacts the cost and speed of AI development, accelerating progress in various AI applications.
The proposed UR-JEPA method offers a more theoretically sound approach to preventing representation collapse than previous methods, potentially leading to more robust and higher-performing AI models.
- · AI researchers
- · Deep learning developers
- · Companies investing in large-scale AI models
- · Inefficient AI training methodologies
Improved stability and performance of self-supervised learning models.
Faster development and deployment of advanced AI applications across various domains, as a key bottleneck in training is mitigated.
Potentially democratizes access to high-performing models by reducing the computational resources previously needed to achieve stable training.
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Read at arXiv cs.LG