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Image-to-Image Translation with Conditional Adversarial Networks
Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Expand
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
A new dataset of human perceptual similarity judgments is introduced and it is found that deep features outperform all previous metrics by large margins on this dataset, and suggests that perceptual similarity is an emergent property shared across deep visual representations. Expand
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
This work presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples, and introduces a cycle consistency loss to push F(G(X)) ≈ X (and vice versa). Expand
Context Encoders: Feature Learning by Inpainting
It is found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures, and can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods. Expand
Colorful Image Colorization
This paper proposes a fully automatic approach to colorization that produces vibrant and realistic colorizations and shows that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. Expand
Image quilting for texture synthesis and transfer
This work uses quilting as a fast and very simple texture synthesis algorithm which produces surprisingly good results for a wide range of textures and extends the algorithm to perform texture transfer — rendering an object with a texture taken from a different object. Expand
Curiosity-Driven Exploration by Self-Supervised Prediction
This work forms curiosity as the error in an agent's ability to predict the consequence of its own actions in a visual feature space learned by a self-supervised inverse dynamics model, which scales to high-dimensional continuous state spaces like images, bypasses the difficulties of directly predicting pixels, and ignores the aspects of the environment that cannot affect the agent. Expand
Ensemble of exemplar-SVMs for object detection and beyond
This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by aExpand
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
A novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model that adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs is proposed. Expand
Fast bilateral filtering for the display of high-dynamic-range images
We present a new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail. It is based on a two-scale decomposition of the image into a base layer,...