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Online Convolutional Sparse Coding with Sample-Dependent Dictionary
TL;DR: A sample-dependent dictionary in which filters are obtained as linear combinations of a small set of base filters learned from the data is proposed, which allows a large number of sample- dependent patterns to be captured, while the resultant model can still be efficiently learned by online learning.
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Abstract: 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.
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Citations
A Randomized Block-Coordinate Adam online learning optimization algorithm
TL;DR: This paper analyzes the convergence of RBC-Adam and obtains the regret bound, $$O(\sqrt{T})$$ O ( T ) , where T is a time horizon.
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Generalized Convolutional Sparse Coding With Unknown Noise
TL;DR: A generalized CSC model capable of handling complicated unknown noise, modeled by the Gaussian mixture model, which can approximate any continuous probability density function, is proposed and obtained the same space complexity and a smaller running time than existing CSC methods.
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Multi-Scale Feedback Convolutional Sparse Coding Network for Saliency Detection in Remote Sensing Images
Zhou Huang,Huai-Xin Chen,Cheng-Wu Bai,Li-Li Yan +3 more
- 11 Jul 2021
TL;DR: Wang et al. as mentioned in this paper proposed a multiscale feedback sparse coding (MFC) network for SOD of optical remote sensing images, where the soft threshold shrinkage (SST) function and the CNN components are first used to construct the CSC block (CSCB), then the multi-scale image representations are fed into the stacked CSCB to extract the features thoroughly.
1
Deep Convolutional Sparse Coding Network for Salient Object Detection in VHR Remote Sensing Images
Xiongxu,Zhou Huang,Huai-Xin Chen,Bi-Yuan Liu +3 more
- 18 Dec 2020
TL;DR: Wang et al. as discussed by the authors combined the advantages of convolutional sparse coding (CSC) and deep neural networks, and proposed a deep CSC network model for saliency object detection in remote sensing images.
References
•Proceedings Article
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue,Yangqing Jia,Oriol Vinyals,Judy Hoffman,Ning Zhang,Eric Tzeng,Trevor Darrell +6 more
- 21 Jun 2014
TL;DR: DeCAF as discussed by the authors is an open-source implementation of these deep convolutional activation features, along with all associated network parameters, to enable vision researchers to conduct experimentation with deep representations across a range of visual concept learning paradigms.
•Book
Proximal Algorithms
Neal Parikh,Stephen Boyd +1 more
- 27 Nov 2013
TL;DR: The many different interpretations of proximal operators and algorithms are discussed, their connections to many other topics in optimization and applied mathematics are described, some popular algorithms are surveyed, and a large number of examples of proxiesimal operators that commonly arise in practice are provided.
4.2K
•Proceedings Article
Efficient sparse coding algorithms
Honglak Lee,Alexis Battle,Rajat Raina,Andrew Y. Ng +3 more
- 04 Dec 2006
TL;DR: These algorithms are applied to natural images and it is demonstrated that the inferred sparse codes exhibit end-stopping and non-classical receptive field surround suppression and, therefore, may provide a partial explanation for these two phenomena in V1 neurons.
•Proceedings Article
Deconvolutional networks
Matthew D. Zeiler,Dilip Krishnan,Graham W. Taylor,Rob Fergus +3 more
- 01 Jun 2010
TL;DR: This work presents a learning framework where features that capture these mid-level cues spontaneously emerge from image data, based on the convolutional decomposition of images under a spar-sity constraint and is totally unsupervised.
2K
Efficient projections onto the l1-ball for learning in high dimensions
John C. Duchi,Shai Shalev-Shwartz,Yoram Singer,Tushar Deepak Chandra +3 more
- 05 Jul 2008
TL;DR: Efficient algorithms for projecting a vector onto the l1-ball are described and variants of stochastic gradient projection methods augmented with these efficient projection procedures outperform interior point methods, which are considered state-of-the-art optimization techniques.