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Existing image generator networks rely heavily on spatial convolutions and, optionally, self-attention blocks in order to gradually synthesize images in a coarse-to-fine manner.
An iteration method for the solution of the eigenvalue problem of linear differential and integral operators
C. Lanczos · 1950
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Compositional pattern producing networks: A novel abstraction of development
K. O. Stanley · 2007
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Differentiable Augmentation for Data-Efficient GAN Training
S. Zhao, Z. Liu, J. Lin, J.-Y. Zhu, and S. Han · 2011
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
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Generating large images from latent vectors
D. Ha · 2016
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Generating large images from latent vectors - part two
D. Ha · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2016
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Pixel recurrent neural networks
A. Van Den Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao · 2016
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Joint face detection and alignment using multitask cascaded convolutional networks
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Spherical cnns
T. S. Cohen, M. Geiger, J. Köhler, and M. Welling · 2018
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Glow: Generative flow with invertible 1x1 convolutions
D. P. Kingma and P. Dhariwal · 2018
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An intriguing failing of convolutional neural networks and the CoordConv solution
R. Liu, J. Lehman, P. Molino, F. Petroski Such, E. Frank, A. Sergeev, and J. Yosinski · 2018
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Which Training Methods for GANs do actually Converge?
L. Mescheder, A. Geiger, and S. Nowozin · 2018
Coco-gan: Generation by parts via conditional coordinating
C. H. Lin, C. Chang, Y. Chen, D. Juan, W. Wei, and H. Chen · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
V. Sitzmann, M. Zollhöfer, and G. Wetzstein · 2019
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Self-attention generative adversarial networks
H. Zhang, I. J. Goodfellow, D. N. Metaxas, and A. Odena · 2019
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Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions
R. Durall, M. Keuper, and J. Keuper · 2020
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Convnetjs demo: Image ”painting”
A. Karpathy · 2020
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Training Generative Adversarial Networks with Limited Data
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila · 2020
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Differentiable image parameterizations
A. Mordvintsev, N. Pezzotti, L. Schubert, and C. Olah · 2018
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Assessing generative models via precision and recall
M. S. M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
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Large scale GAN training for high fidelity natural image synthesis
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A style-based generator architecture for generative adversarial networks
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Improved precision and recall metric for assessing generative models
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Analyzing and improving the image quality of stylegan
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
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GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis
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Implicit Neural Representations with Periodic Activation Functions
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Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
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