A Group-Theoretic Framework for Data Augmentation

25 July 2019

Papers citing "A Group-Theoretic Framework for Data Augmentation"

9 / 9 papers shown

Title
Tilt your Head: Activating the Hidden Spatial-Invariance of Classifiers Johann Schmidt Sebastian Stober 43 1 0 06 May 2024
Quantifying the Variability Collapse of Neural Networks Jing-Xue Xu Haoxiong Liu 36 4 0 06 Jun 2023
Unproportional mosaicing Vojtech Molek P. Hurtík Pavel Vlasánek D. Adamczyk 24 1 0 03 Mar 2023
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? Henry Kvinge Tegan H. Emerson Grayson Jorgenson Scott Vasquez T. Doster Jesse D. Lew 45 8 0 07 Oct 2022
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 130 78 0 01 Oct 2021
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li Ruosong Wang Dingli Yu S. Du Wei Hu Ruslan Salakhutdinov Sanjeev Arora 16 131 0 03 Nov 2019
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen Mario Geiger Maurice Weiler MLT AI4CE 165 308 0 05 Nov 2018
Data augmentation instead of explicit regularization Alex Hernández-García Peter König 30 141 0 11 Jun 2018
RenderGAN: Generating Realistic Labeled Data Leon Sixt Benjamin Wild Tim Landgraf GAN 158 176 0 04 Nov 2016