# Online Convolutional Sparse Coding with Sample-Dependent Dictionary

arXiv: https://arxiv.org/abs/1804.10366 · HTML: https://mlsanity.com/paper/1804.10366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Training-Adaptive Convolutional Sparse Coding via Information Bottleneck for Robust Visual Representation](https://mlsanity.com/paper/2609.19122/) (2026)

## Abstract (arXiv)

Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited scalability. In this paper, instead of convolving with a dictionary shared by all samples, we propose the use of a sample-dependent dictionary in which filters are obtained as linear combinations of a small set of base filters learned from the data. This added flexibility allows a large number of sample-dependent patterns to be captured, while the resultant model can still be efficiently learned by online learning. Extensive experimental results show that the proposed method outperforms existing CSC algorithms with significantly reduced time and space requirements.