arXiv — Machine Learning · · 3 min read

Attention-Only White-Box Transformer via LeJEPA-Based Self-Supervised Pretraining

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Computer Science > Machine Learning

arXiv:2608.04213 (cs)
[Submitted on 4 Aug 2026]

Title:Attention-Only White-Box Transformer via LeJEPA-Based Self-Supervised Pretraining

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Abstract:Existing studies on self-supervised learning for white-box networks typically decouple the derivation of white-box networks via optimization algorithms from self-supervised learning paradigms. In this work, we instead revisit the two components from a joint perspective. The LeJEPA-based self-supervised framework assumes an isotropic Gaussian distribution as the optimal embedding distribution for downstream tasks, which is conceptually equivalent to the expansion term $R(Z)$ in the sparse rate reduction objective guiding white-box Transformer optimization. Building on this observation, we use the LeJEPA self-supervised paradigm to optimize $R(Z)$, and derive the remaining terms $R^{c}(Z\mid U_{[K]})+\lambda\lVert Z\rVert_{0}$ via the alternating direction method of multipliers (ADMM) into an attention-only Transformer that dispenses with the ISTA structure or MLP layers of the original design. Experimental results demonstrate that our attention-only white-box Transformer achieves classification accuracies of $88.88\%$ on CIFAR-10 and $63.54\%$ on CIFAR-100 at the Base scale under the LeJEPA self-supervised paradigm, while the original white-box Transformer CRATE achieves classification accuracies of $89.18\%$ on CIFAR-10 and $63.56\%$ on CIFAR-100. Our model achieves competitive performance while reducing the parameter count by roughly $31\%$. Beyond the white-box setting, we further investigate standard ViTs and find that replacing all MLP blocks with ReLU activations under knowledge distillation removes approximately 66\% of the parameters while preserving competitive accuracy, motivating further investigation into the potential redundancy of MLP modules in standard ViT architectures.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2608.04213 [cs.LG]
  (or arXiv:2608.04213v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.04213
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yang Bai [view email]
[v1] Tue, 4 Aug 2026 20:26:12 UTC (5,376 KB)
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