2.3k citations · 7.8k across the 98 of their papers we have counts for
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Learning Shape Abstractions by Assembling Volumetric Primitives
Shubham Tulsiani, Hao Su, Leonidas J. Guibas +2
We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and geometrically in…
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Richard Zhang, Phillip Isola, Alexei A. Efros
We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split t…
Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou +1
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to…
Generative Visual Manipulation on the Natural Image Manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman +1
Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user…
A 4D Light-Field Dataset and CNN Architectures for Material Recognition
Ting-Chun Wang, Jun-Yan Zhu, Ebi Hiroaki +3
We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset co…
What makes ImageNet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, Alexei A. Efros
The tremendous success of ImageNet-trained deep features on a wide range of transfer tasks begs the question: what are the properties of the ImageNet dataset that are critical for…
View Synthesis by Appearance Flow
Tinghui Zhou, Shubham Tulsiani, Weilun Sun +2
We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints. We approach this as a…
Learning Dense Correspondence via 3D-guided Cycle Consistency
Tinghui Zhou, Philipp Krähenbühl, Mathieu Aubry +2
Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or…
Context Encoders: Feature Learning by Inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue +2
We present an unsupervised visual feature learning algorithm driven by context-based pixel prediction. By analogy with auto-encoders, we propose Context Encoders -- a convolutional…
Colorful Image Colorization
Richard Zhang, Phillip Isola, Alexei A. Efros
Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so pre…