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Image-to-Image Translation with Conditional Adversarial Networks
TLDR
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
Unsupervised Learning of Depth and Ego-Motion from Video
TLDR
Empirical evaluation demonstrates the effectiveness of the unsupervised learning framework for monocular depth performs comparably with supervised methods that use either ground-truth pose or depth for training, and pose estimation performs favorably compared to established SLAM systems under comparable input settings. Expand
Rethinking the Value of Network Pruning
TLDR
It is found that with optimal learning rate, the "winning ticket" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization, and the need for more careful baseline evaluations in future research on structured pruning methods is suggested. Expand
View Synthesis by Appearance Flow
TLDR
This work addresses the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints and shows that for both objects and scenes, this approach is able to synthesize novel views of higher perceptual quality than previous CNN-based techniques. Expand
Everybody Dance Now
This paper presents a simple method for “do as I do” motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutesExpand
Stereo Magnification: Learning View Synthesis using Multiplane Images
TLDR
This paper explores an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including VR cameras and now-widespread dual-lens camera phones, and proposes a learning framework that leverages a new layered representation that is called multiplane images (MPIs). Expand
Undoing the Damage of Dataset Bias
TLDR
Overall, this work finds that it is beneficial to explicitly account for bias when combining multiple datasets, and proposes a discriminative framework that directly exploits dataset bias during training. Expand
Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency
TLDR
A differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view is proposed by reformulating view consistency using a differentiable ray consistency (DRC) term and it is shown that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations. Expand
Learning Data-Driven Reflectance Priors for Intrinsic Image Decomposition
TLDR
A model is trained to predict relative reflectance ordering between image patches from large-scale human annotations, producing a data-driven reflectance prior and it is shown how to naturally integrate this learned prior into existing energy minimization frame-works for intrinsic image decomposition. Expand
Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency
TLDR
A differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view is proposed by reformulating view consistency using a differentiable ray consistency (DRC) term and it is shown that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations. Expand
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