Attention-Only White-Box Transformer Achieves Competitive Performance
Key takeaways
- An attention-only Transformer can achieve competitive performance with fewer parameters.
- LeJEPA-based self-supervised pretraining is effective for optimizing white-box networks.
- MLP layers in Transformers might be significantly redundant, offering optimization opportunities.
- This approach could lead to more efficient and deployable AI models.
Who benefits
Summary
Researchers developed an attention-only white-box Transformer, optimized through LeJEPA-based self-supervised pretraining, which removes MLP layers and reduces parameter count by 31% while maintaining competitive classification accuracy on CIFAR datasets. This work explores the potential redundancy of MLP modules in standard ViT architectures.
Why it matters
This research offers a path to more efficient and interpretable Transformer models by reducing complexity and parameter count, which can lead to faster training, lower inference costs, and easier deployment on resource-constrained devices.
How to implement this in your domain
- 1Evaluate the feasibility of adopting attention-only Transformer architectures for specific vision tasks.
- 2Experiment with LeJEPA-based self-supervised pretraining for model initialization.
- 3Investigate replacing MLP blocks with simpler activation functions in existing ViT models to reduce parameters.
- 4Benchmark the performance and efficiency gains on proprietary datasets and hardware.
Original post by Yang Bai, Linyuan Wang, Haoyang Jiang, Nuolin Sun, Libin Hou, Bin Yan
"arXiv:2608.04213v1 Announce Type: new 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 tw…"
View on XOriginally posted by Yang Bai, Linyuan Wang, Haoyang Jiang, Nuolin Sun, Libin Hou, Bin Yan on X · view source
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