Deep Learning Theory for Computer Vision

What is the interplay of width/depth and how does the initialization affects the robustness to adversarial attacks? What is a principled heuristic for selecting good architectures in Neural Architecture Search (NAS)? What is the role of Fourier features in implicit neural representations (INRs)? In this tutorial, we aim to build a bridge between the empirical performance of neural networks and deep learning theory. In particular, we want to make the recent deep learning (DL) theory developments accessible to vision researchers, and motivate vision researchers to design new architectures and algorithms for practical tasks. In the first part of the tutorial, we will discuss popular notions in DL theory, such as lazy training and Neural Tangent Kernel (NTK), or bilevel optimization for adversarial attacks and NAS. Then, we will exhibit how such tools can be critical in understanding the inductive bias of networks.