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We propose a novel approach to learning the generative neural fields represented by linear combinations of implicit basis networks.
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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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Fast and accurate deep network learning by exponential linear units (ELUs)
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Hypernetworks
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Learning representations and generative models for 3d point clouds
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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FiLM: Visual reasoning with a general conditioning layer
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Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
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DeepSDF: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Fast context adaptation via meta-learning
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Fourier features let networks learn high frequency functions in low dimensional domains
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pi-GAN: Periodic implicit generative adversarial networks for 3d-aware image synthesis
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Z.-H. Lin, W.-C. Ma, H.-Y. Hsu, Y.-C. F. Wang, and S. Wang · 2022
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Beyond Periodicity: Towards a unifying framework for activations in coordinate-mlps
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Neural implicit dictionary via mixture-of-expert training
P. Wang, Z. Fan, T. Chen, and Z. Wang · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
M. Wortsman, G. Ilharco, S. Y. Gadre, R. Roelofs, R. Gontijo-Lopes, A. S. Morcos, H. Namkoong, A. Farhadi, Y. Carmon, S. Kornblith, and L. Schmidt · 2022
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Efficient geometry-aware 3d generative adversarial networks
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Model development with vessl, 2023
J. An · 2023
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