Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
arXiv:2403.04545v3 Announce Type: replace Abstract: Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free architectures. While prior work has primarily examined scaling effects from an optimization perspective, this paper investigates their role in residual architectures through the lens of generalization theory. Specifically, we establish that wide residual networks (ResNets) with constant scaling factors become asymptotically unlearnable as depth increases. In contrast, when the scaling factor exhibit
This research provides a theoretical understanding of how architectural choices in AI models, specifically residual branch scaling, impact generalization, building on recent empirical successes in normalization-free networks.
Understanding the architectural principles that improve AI generalization is crucial for developing more robust and efficient models, directly impacting the performance ceiling of AI systems across various applications.
The theoretical insight into branch scaling as an implicit architectural regularization offers a new design principle for AI practitioners, potentially steering future ResNet development towards more effective generalization strategies.
- · AI researchers
- · Neural network developers
- · AI software companies
- · Developers using suboptimal ResNet architectures
- · AI projects with poor generalization properties
Improved understanding and design of deep learning architectures, particularly ResNets.
Development of more generalizable and efficient AI models leading to faster development cycles.
Accelerated progress in complex AI applications requiring highly robust and generalizable models, such as autonomous systems or scientific discovery tools.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG