Project 42: Optimization for the Multi-layer Convolutional Sparse Coding
Contact Information:
Assoc. Prof. Wenrui Dai
Email: daiwenrui@sjtu.edu.cn
Project Description and Objectives:
Convolutional sparse coding (CSC) has been demonstrated to facilitate the representation of high-dimensional visual signals in the tasks of image classification, visual recognition, image reconstruction, and feature extraction. This unsupervised method improves the efficiency of sparse representation by posing a global model with localized dictionaries. Recently, online learning and consensus optimization have been studied to enable scalability in high-dimensional feature learning, but still suffer from a degraded reconstruction performance for multi-layer cases. This project aims to develop a multi-layer dictionary learning method for multi-layer CSC. This optimization method is supposed to guarantee convergence with a bounded approximation error under the varying sparsity requirement for multiple layers. Furthermore, this project also plans to study the connection between multi-layer dictionary learning and deep convolutional neural networks.
Eligibility Requirements:
Basic knowledge of signals and systems, digital signal processing, digital image processing, matrix analysis, and optimization theory.
Mastery of more than one programming language, C/C++ and MATLAB preferred.
Main Tasks:
Develop a multi-layer dictionary learning method for multi-layer convolutional sparse coding.
Analyze the convergence condition and approximation error of the proposed method.
Establish its connection with deep convolutional neural networks for interpretability.
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