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Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications.
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Meshlab: an open-source mesh processing tool
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A unified architecture for natural language processing: Deep neural networks with multitask learning
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Imagenet: A large-scale hierarchical image database
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Recent advances in deep learning for speech research at microsoft
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Explaining and harnessing adversarial examples
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Adam: A method for stochastic optimization
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Microsoft coco: Common objects in context
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Opendr: An approximate differentiable renderer
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Beyond pascal: A benchmark for 3d object detection in the wild
Y. Xiang, R. Mottaghi, and S. Savarese · 2014
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Shape, illumination, and reflectance from shading
J. T. Barron and J. Malik · 2015
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ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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Synthesizing training images for boosting human 3d pose estimation
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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
H. Su, C. R. Qi, Y. Li, and L. J. Guibas · 2015
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Densely connected convolutional networks
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Scenenet rgb-d: Can 5m synthetic images beat generic imagenet pre-training on indoor segmentation?
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Automatic differentiation in pytorch
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Semantic scene completion from a single depth image
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Learning from synthetic humans
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A. K. F. Y. L. Z. X. T. J. X. Z. Wu, S. Song · 2015
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Rendering resources, 2016
B. Bitterli · 2016
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Understanding realworld indoor scenes with synthetic data
A. Handa, V. Patraucean, V. Badrinarayanan, S. Stent, and R. Cipolla · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Deep exemplar 2d-3d detection by adapting from real to rendered views
F. Massa, B. Russell, and M. Aubry · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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X. Zeng, C. Liu, W. Qiu, L. Xie, Y.-W. Tai, C. K. Tang, and A. L. Yuille · 2017
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Physically-based rendering for indoor scene understanding using convolutional neural networks
Y. Zhang, S. Song, E. Yumer, M. Savva, J.-Y. Lee, H. Jin, and T. Funkhouser · 2017
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Synthesizing robust adversarial examples
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok · 2018
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Physical adversarial examples for object detectors
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, F. Tramer, A. Prakash, T. Kohno, and D. Song · 2018
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Unsupervised training for 3d morphable model regression
K. Genova, F. Cole, A. Maschinot, A. Sarna, D. Vlasic, and W. T. Freeman · 2018
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Neural 3d mesh renderer
H. Kato, Y. Ushiku, and T. Harada · 2018
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Pytorch implememtation of the neural mesh renderer
N. Kolotouros · 2018
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Differentiable monte carlo ray tracing through edge sampling
T.-M. Li, M. Aittala, F. Durand, and J. Lehtinen · 2018
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Differentiable image parameterizations
A. Mordvintsev, N. Pezzotti, L. Schubert, and C. Olah · 2018
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Rendernet: A deep convolutional network for differentiable rendering from 3d shapes
T. H. Nguyen-Phuoc, C. Li, S. Balaban, and Y. Yang · 2018
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Yolov3: An incremental improvement
J. Redmon and A. Farhadi · 2018
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Generating adversarial examples with adversarial networks
C. Xiao, B. Li, J. yan Zhu, W. He, M. Liu, and D. Song · 2018
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Shape from shading through shape evolution
D. Yang and J. Deng · 2018
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Beyond pixel norm-balls: Parametric adversaries using an analytically differentiable renderer
H.-T. D. Liu, M. Tao, C.-L. Li, D. Nowrouzezahrai, and A. Jacobson · 2019
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