A Group-Theoretic Framework for Data Augmentation

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A Group-Theoretic Framework for Data Augmentation

25 July 2019

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

18 / 18 papers shown

Title
Tilt your Head: Activating the Hidden Spatial-Invariance of ClassifiersInternational Conference on Machine Learning (ICML), 2024 Johann Schmidt Sebastian Stober 260 4 0 06 May 2024
Unproportional mosaicing Vojtech Molek P. Hurtík Pavel Vlasánek D. Adamczyk 225 1 0 03 Mar 2023
Data-Efficient Augmentation for Training Neural NetworksNeural Information Processing Systems (NeurIPS), 2022 Tian Yu Liu Baharan Mirzasoleiman 173 8 0 15 Oct 2022
In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?Neural Information Processing Systems (NeurIPS), 2022 Henry Kvinge Tegan H. Emerson Grayson Jorgenson Scott Vasquez T. Doster Jesse D. Lew 186 11 0 07 Oct 2022
Learning Continuous Rotation Canonicalization with Radial Beam Sampling J. Schmidt Sebastian Stober 222 4 0 21 Jun 2022
Learning Instance-Specific Augmentations by Capturing Local InvariancesInternational Conference on Machine Learning (ICML), 2022 Ning Miao Tom Rainforth Emile Mathieu Yann Dubois Yee Whye Teh Adam Foster Hyunjik Kim 403 14 0 31 May 2022
Reconstruction for Powerful Graph Representations Leonardo Cotta Christopher Morris Bruno Ribeiro AI4CE 407 90 0 01 Oct 2021
Model-Based Domain GeneralizationNeural Information Processing Systems (NeurIPS), 2021 Avi Schwarzschild George J. Pappas Hamed Hassani OOD 331 148 0 23 Feb 2021
Data augmentation and image understanding Alex Hernandez-Garcia 134 6 0 28 Dec 2020
How Data Augmentation affects Optimization for Linear Regression Boris Hanin Yi Sun 205 19 0 21 Oct 2020
Improving Transformation Invariance in Contrastive Representation LearningInternational Conference on Learning Representations (ICLR), 2020 Adam Foster Rattana Pukdee Tom Rainforth 239 25 0 19 Oct 2020
WeMix: How to Better Utilize Data Augmentation Yi Tian Xu Asaf Noy Ming Lin Qi Qian Hao Li Rong Jin 149 18 0 03 Oct 2020
On the Generalization Effects of Linear Transformations in Data AugmentationInternational Conference on Machine Learning (ICML), 2020 Sen Wu Hongyang R. Zhang Gregory Valiant Christopher Ré 281 87 0 02 May 2020
On the Benefits of Invariance in Neural Networks Clare Lyle Mark van der Wilk Marta Z. Kwiatkowska Y. Gal Benjamin Bloem-Reddy OOD BDL 261 99 0 01 May 2020
Implicit Regularization and Convergence for Weight NormalizationNeural Information Processing Systems (NeurIPS), 2019 Xiaoxia Wu Guang Cheng Zhaolin Ren Shanshan Wu Zhiyuan Li Suriya Gunasekar Rachel A. Ward Qiang Liu 473 26 0 18 Nov 2019
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li Ruosong Wang Dingli Yu S. Du Wei Hu Ruslan Salakhutdinov Sanjeev Arora 182 136 0 03 Nov 2019
Implicit Rugosity Regularization via Data Augmentation Daniel LeJeune Randall Balestriero Hamid Javadi Richard G. Baraniuk 190 4 0 28 May 2019
Data augmentation instead of explicit regularization Alex Hernández-García Peter König 225 156 0 11 Jun 2018