cvpr2024-tutorial-low-dim-models.github.io - Learning Deep Low-Dimensional Models from High-Dimensional Data: From Theory to Practice

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Learning Deep Low-Dimensional Models from High-Dimensional Data: From Theory to Practice CVPR 2024 Tutorial Date: Tuesday, June 18 (full day tutorial) Location: Room 3 Overview Over the past decade, the advent of machine learning and large-scale computing has immeasurably changed the ways we process, interpret, and predict with data in imaging and computer vision. The “traditional” approach to algorithm design, based around parametric models for specific structures of signals and measurements—say sparse and

As such, this tutorial provides a timely tutorial that uniquely bridges low-dimensional models with deep learning in imaging and vision. This tutorial will show how:

We will begin by introducing fundamental low-dimensional models (e.g., basic sparse and low-rank models) with motivating computer vision applications. Based on these developments, we will discuss strong conceptual, algorithmic, and theoretical connections between low-dimensional structures and deep models, providing new perspectives to understand state-of-the-art deep models in terms of learned representations, generalizability, and transferability. Finally, we will demonstrate that these connections can le

Links to cvpr2024-tutorial-low-dim-models.github.io (2)

• sdbuchanan.com Sam D. Buchanan
• delta-lab-ai.github.io Welcome to the DeLTA Lab