Michael Elad

Sparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk, we start by presenting this model and its features, and then turn to describe two special cases of it: 1) the convolutional sparse coding (CSC) and 2) its multi-layered version (ML-CSC). Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to deep-learning. Alongside this main message of bringing a theoretical backbone to deep-learning, another central message that will accompany us throughout the talk. Generative models for describing data sources enable a systematic way to design algorithms, while also providing a complete mechanism for a theoretical analysis of these algorithms' performance. This talk is meant for newcomers to this field and no prior knowledge on sparse approximation is assumed.

Sparse approximation is a well-established theory, with a profound impact on the fields of signal and image processing. In this talk, we start by presenting this model and its features, and then turn to describe two special cases from it: 1) the convolutional sparse coding (CSC) and 2) its multi-layered version (ML-CSC). Amazingly, as we will carefully show, ML-CSC provides a solid theoretical foundation to deep-learning architectures. Alongside this main message of bringing a theoretical backbone to deep-learning, another central message that will accompany us throughout the talk is this: "Generative models for describing data sources enable a systematic way to design algorithms, while also providing a complete mechanism for a theoretical analysis of these algorithms’ performance." This talk is meant for newcomers to this field and no prior knowledge on sparse approximation is assumed.