Sparse Coding with Structured Sparsity Priors and Multilayer Architecture for Image Classification

Applying sparse coding on large dataset for image classification is a long standing problem in the field of computer vision. It has been found that the sparse coding models exhibit disappointing performance on these large datasets where variability is broad and anomalies are common. Conversely, deep neural networks thrive on bountiful data. Their success has encouraged researchers to try and augment the learning capacity of traditionally shallow sparse coding methods by adding layers. Multilayer sparse coding networks are expected to combine the best of both sparsity regularizations and deep architectures. To date, however, endeavors to marry the two techniques have not achieved significant improvements over their individual counterparts. In this thesis, we first briefly review multiple structured sparsity priors as well as various supervised dictionary learning techniques with applications on hyperspectral image classification. Based on the structured sparsity priors and dictionary learning techniques, we then develop a novel multilayer sparse coding network that contains thirteen sparse coding layers. The proposed sparse coding network learns both the dictionaries and the regularization parameters simultaneously using an end-to-end supervised learning scheme. We show empirical evidence that the regularization parameters can adapt to the given training data. We also propose applying dimension reduction within sparse coding networks to dramatically reduce the output dimensionality of the sparse coding layers and mitigate computational costs. Moreover, our sparse coding network is compatible with other powerful deep learning techniques such as drop out, batch normalization and shortcut connections. Experimental results show that the proposed multilayer sparse coding network produces classification accuracy competitive with the deep neural networks while using significantly fewer parameters and layers

1,2-Di-tert-butyl­ethane-1,2-diyl bis­(tert-butane­sulfinamide)

In the title compound, C18H40N2O2S2, a vicinal diamine derivative, the crystal structure is stabilized by two intra­molecular N—H⋯O hydrogen bonds. The distance between the two kernel chiral C atoms is 1.580 (2) Å