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Learning Deep Low-Dimensional Models from High-Dimensional Data: From Theory to Practice

cvpr2024-tutorial-low-dim-models.github.io · 1,189 words · saved by 1 readers

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 low-rank models—and the associated optimization toolkit, is now significantly enriched with data-driven learning-based techniques, where large-scale networks are pre-trained and then adapted to a variety of specific tasks. Nevertheless, the successes of both modern data-driven and classic model-based paradigms rely crucially on correctly identifying the low-dimensional structures present in real-world data, to the extent that we see the roles of learning and compression of data processing algorithms—whether explicit or implicit, as with deep networks—as inextricably linked. As such, this tutorial provides a timely tutorial that uniquely bridges low-dimensi

Learning Deep Low-Dimensional Models from High-Dimensional Data: From Theory to Practice --> 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 (Summit 442) tag --> 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 m

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