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20132023
most citedImproving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

61 citations · 215 across the 32 of their papers we have counts for

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Showing 2019Show all

13 papers · 1 filter

cs.CV2019

Discriminative Dimension Reduction based on Mutual Information

Orod Razeghi, Guoping Qiu

The "curse of dimensionality" is a well-known problem in pattern recognition. A widely used approach to tackling the problem is a group of subspace methods, where the original feat…

cs.CV2019★ 1 cited

Object Recognition with Human in the Loop Intelligent Frameworks

Orod Razeghi, Guoping Qiu

Classifiers embedded within human in the loop visual object recognition frameworks commonly utilise two sources of information: one derived directly from the imagery data of an obj…

cs.CV2019

Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation

Xianxu Hou, Jingxin Liu, Bolei Xu +6

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training…

cs.LG2019

Spectral Regularization for Combating Mode Collapse in GANs

Kanglin Liu, Wenming Tang, Fei Zhou +1

Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs).In this paper, we present spectral regularizati…

cs.CV2019★ 61 cited

Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training

Xianxu Hou, Ke Sun, Linlin Shen +1

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output…

eess.IV2019

Learning Deep Image Priors for Blind Image Denoising

Xianxu Hou, Hongming Luo, Jingxin Liu +5

Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realis…