Year: 2021
Type: preprint
Abstract: Deep neural networks (NN) have achieved great success in many applications. However, why do deep neural networks obtain good generalization at an over-parameterization regime is still unclear. To better understand deep NN, we establish the connection between deep NN and a novel kernel family, i.e., Neural Optimization Kernel (NOK). The architecture of structured approximation of NOK performs monotonic descent updates of implicit regularization problems. We can implicitly choose the regularization problems by employing different activation functions, e.g., ReLU, max pooling, and soft-thresholding. We further establish a new generalization bound of our deep structured approximated NOK architecture. Our unsupervised structured approximated NOK block can serve as a simple plug-in of popular backbones for a good generalization against input noise.
Source: arXiv (Cornell University)
Authors: Lyu, Yueming, Tsang, Ivor
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3170079870