Journal ArticleDOI
Multi-scale semantic image inpainting with residual learning and GAN
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TLDR
A combination of an encoder–decoder generator for image semantic inpainting and a multi-layer convolutional net for image seamless fusion, which is capable of restoring image effectively and seamlessly.About:
This article is published in Neurocomputing.The article was published on 2019-02-28. It has received 32 citations till now. The article focuses on the topics: Inpainting & Context (language use).read more
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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
Journal ArticleDOI
Image Inpainting: A Review
TL;DR: The work in this paper was made by NPRP grant # NPRP8-140-2-065 from the Qatar National Research Fund (a member of the Qatar Foundation).
Journal ArticleDOI
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for image-to-image transformation
TL;DR: The proposed CSGAN uses a new objective function (loss) called Cyclic-Synthesized Loss (CS) between the synthesized image of one domain and cycled image of another domain and exhibits the promising and comparable performance over Facades dataset in terms of both qualitative and quantitative measures.
Journal ArticleDOI
Image inpainting: A review
TL;DR: A brief review of the existing image inpainting approaches can be found in this paper, where the main contribution is the presentation of the three categories of image in-painting methods along with a list of available datasets to evaluate their proposed methodology against.
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Image inpainting based on deep learning: A review
TL;DR: Zhang et al. as discussed by the authors reviewed the specific research status of deep learning technology in the field of image inpainting in the past 15 years; then, they deeply studied and analyzed the existing image restoration methods based on different neural network structures and their information fusion methods.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).