Training Generative Adversarial Networks by Solving Ordinary
Differential Equations

Training Generative Adversarial Networks by Solving Ordinary Differential Equations

28 October 2020

Jost Tobias Springenberg

Timothy Lillicrap

Papers citing "Training Generative Adversarial Networks by Solving Ordinary Differential Equations"

10 / 10 papers shown

Title
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning F. Mumuni A. Mumuni AAML 37 5 0 11 Mar 2024
On a continuous time model of gradient descent dynamics and instability in deep learning Mihaela Rosca Yan Wu Chongli Qin Benoit Dherin 18 7 0 03 Feb 2023
Why neural networks find simple solutions: the many regularizers of geometric complexity Benoit Dherin Michael Munn M. Rosca David Barrett 55 31 0 27 Sep 2022
The Geometric Occam's Razor Implicit in Deep Learning Benoit Dherin Micheal Munn David Barrett 22 6 0 30 Nov 2021
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision I. Higgins Peter Wirnsberger Andrew Jaegle Aleksandar Botev 47 8 0 10 Nov 2021
Training Generative Adversarial Networks with Adaptive Composite Gradient Huiqing Qi Fang Li Shengli Tan Xiangyun Zhang GAN 26 3 0 10 Nov 2021
Which priors matter? Benchmarking models for learning latent dynamics Aleksandar Botev Andrew Jaegle Peter Wirnsberger Daniel Hennes I. Higgins AI4CE 35 28 0 09 Nov 2021
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach Yuanhao Wang Guodong Zhang Jimmy Ba 33 100 0 16 Oct 2019
A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras S. Laine Timo Aila 306 10,368 0 12 Dec 2018
A Learned Representation For Artistic Style Vincent Dumoulin Jonathon Shlens M. Kudlur GAN 214 1,156 0 24 Oct 2016