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The deep neural networks used in modern computer vision systems require enormous image datasets to train them.
Computer rendering of stochastic models
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Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 2019
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André Luiz Marasca, Dalcimar Casanova, and Marcelo Teixeira · 2019
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Synthetic data for deep learning, 2019
Sergey I. Nikolenko · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Exploring the Limits of Weakly Supervised Pretraining
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Training Deep Networks With Synthetic Data: Bridging the Reality Gap by Domain Randomization
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Generating Artificial Data for Private Deep Learning
Aleksei Triastcyn and Boi Faltings · 2019
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Hydra - a framework for elegantly configuring complex applications
Omry Yadan · 2019
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Expression of fractals through neural network functions
Nadav Dym, Barak Sober, and Ingrid Daubechies · 2020
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Pre-training without Natural Images
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Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Ryan Steed and Aylin Caliskan · 2020
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Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, and Quoc Le · 2020
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Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
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