Directly Constructing Low-Dimensional Solution Subspaces in DNNs
Published:Dec 29, 2025 12:13
•1 min read
•ArXiv
Analysis
This paper addresses the redundancy in deep neural networks, where high-dimensional widths are used despite the low intrinsic dimension of the solution space. The authors propose a constructive approach to bypass the optimization bottleneck by decoupling the solution geometry from the ambient search space. This is significant because it could lead to more efficient and compact models without sacrificing performance, potentially enabling 'Train Big, Deploy Small' scenarios.
Key Takeaways
- •Addresses the redundancy of high-dimensional widths in DNNs.
- •Proposes a constructive approach to bypass optimization bottlenecks.
- •Demonstrates significant compression of the classification head with minimal performance loss.
- •Introduces Subspace-Native Distillation as a novel paradigm.
- •Aims to enable 'Train Big, Deploy Small' scenarios.
Reference
“The classification head can be compressed by even huge factors of 16 with negligible performance degradation.”