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20102022
most citedRobust Recovery of Subspace Structures by Low-Rank Representation

3.7k citations · 13.3k across the 162 of their papers we have counts for

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Showing 2019Show all

19 papers · 1 filter

cs.CV2019★ 25 cited

RC-DARTS: Resource Constrained Differentiable Architecture Search

Xiaojie Jin, Jiang Wang, Joshua Slocum +4

Recent advances show that Neural Architectural Search (NAS) method is able to find state-of-the-art image classification deep architectures. In this paper, we consider the one-shot…

cs.CV2019★ 3 cited

Very Long Natural Scenery Image Prediction by Outpainting

Zongxin Yang, Jian Dong, Ping Liu +2

Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between…

cs.LG2019★ 1 cited

Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space

Jiahuan Ren, Zhao Zhang, Sheng Li +4

In this paper, we investigate the robust dictionary learning (DL) to discover the hybrid salient low-rank and sparse representation in a factorized compressed space. A Joint Robust…

cs.CV2019★ 82 cited

Asymmetric GAN for Unpaired Image-to-image Translation

Yu Li, Sheng Tang, Rui Zhang +3

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN pro…

cs.CV2019★ 14 cited

Efficient Differentiable Neural Architecture Search with Meta Kernels

Shoufa Chen, Yunpeng Chen, Shuicheng Yan +1

The searching procedure of neural architecture search (NAS) is notoriously time consuming and cost prohibitive.To make the search space continuous, most existing gradient-based NAS…

cs.CV2019★ 3 cited

Compressed DenseNet for Lightweight Character Recognition

Zhao Zhang, Zemin Tang, Yang Wang +3

Convolutional Recurrent Neural Network (CRNN) is a popular network for recognizing texts in images. Advances like the variant of CRNN, such as Dense Convolutional Network with Conn…