Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh,Chen Chen,Teck Yian Lim,Alexander G. Schwing,Alexander G. Schwing,Mark Hasegawa-Johnson,Minh N. Do +6 more
- pp 6882-6890
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TLDR
A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.Abstract:
Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.read more
Citations
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Journal ArticleDOI
Deep learning-based solvability of underdetermined inverse problems in medical imaging
TL;DR: In this paper, the causal relationship regarding the structure of the training data suitable for deep learning, to solve highly under-determined problems is explained, where two approaches use the prior information of the solution in a completely different way.
Proceedings ArticleDOI
Boosted GAN with Semantically Interpretable Information for Image Inpainting
TL;DR: Experimental results show that the proposed boosted GAN with semantically interpretable information for image inpainting can preserve consistency on both attribute and segmentation level, and significantly outperforms the state-of-the-art models.
Book ChapterDOI
Road Layout Understanding by Generative Adversarial Inpainting
TL;DR: A GAN-based semantic segmentation inpainting model is pursued to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings.
Journal ArticleDOI
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
TL;DR: In this paper, an adaptive image sampling algorithm based on deep learning is proposed to speed up the X-Ray fluorescence (XRF) image scanning process, which consists of an adaptive sampling mask generation network which is jointly trained with an image inpainting network.
Posted Content
Chest X-ray Inpainting with Deep Generative Models.
TL;DR: This paper investigates the performance of three recently published deep learning based inpainted models: context encoders, semantic image inpainting, and the contextual attention model, applied to chest x-rays, as the chest exam is the most commonly performed radiological procedure.
References
More filters
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.