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Image-to-Image Translation with Conditional Adversarial Networks
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 output image, butExpand
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Unsupervised Learning of Depth and Ego-Motion from Video
We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use anExpand
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Rethinking the Value of Network Pruning
Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model),Expand
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View Synthesis by Appearance Flow
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 task but,Expand
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Undoing the Damage of Dataset Bias
The presence of bias in existing object recognition datasets is now well-known in the computer vision community. While it remains in question whether creating an unbiased dataset is possible givenExpand
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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
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Multi-view Supervision for Single-View Reconstruction via Differentiable Ray Consistency
We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from anExpand
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Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information
We propose a new matrix completion algorithm— Kernelized Probabilistic Matrix Factorization (KPMF), which effectively incorporates external side information into the matrix factorization process.Expand
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Learning Data-Driven Reflectance Priors for Intrinsic Image Decomposition
We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this as a two-stageExpand
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FlowWeb: Joint image set alignment by weaving consistent, pixel-wise correspondences
Given a set of poorly aligned images of the same visual concept without any annotations, we propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a FlowWebExpand
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