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Revisiting Sparse Convolutional Model for Visual Recognition
xili dai · Mingyang Li · Pengyuan Zhai · Shengbang Tong · Xingjian Gao · Shao-Lun Huang · Zhihui Zhu · Chong You · Yi Ma

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #236

Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sparse convolutional models, which assume that a signal can be expressed by a linear combination of a few elements from a convolutional dictionary, are powerful tools for analyzing natural images with good theoretical interpretability and biological plausibility. However, such principled models have not demonstrated competitive performance when compared with empirically designed deep networks. This paper revisits the sparse convolutional modeling for image classification and bridges the gap between good empirical performance (of deep learning) and good interpretability (of sparse convolutional models). Our method uses differentiable optimization layers that are defined from convolutional sparse coding as drop-in replacements of standard convolutional layers in conventional deep neural networks. We show that such models have equally strong empirical performance on CIFAR-10, CIFAR-100 and ImageNet datasets when compared to conventional neural networks. By leveraging stable recovery property of sparse modeling, we further show that such models can be much more robust to input corruptions as well as adversarial perturbations in testing through a simple proper trade-off between sparse regularization and data reconstruction terms.

Author Information

xili dai (Hong Kong University of Science and Technology)
Mingyang Li (Tsinghua University, Tsinghua University)
Pengyuan Zhai (Harvard University, Harvard University)
Shengbang Tong (University of California Berkeley)
Xingjian Gao (University of California, Berkeley)
Shao-Lun Huang (Tsinghua University, Tsinghua University)
Zhihui Zhu (University of Denver)
Chong You (University of California, Berkeley)
Yi Ma (UC Berkeley)

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