Learning Instance-Specific Augmentations by Capturing Local Invariances

31 May 2022

Papers citing "Learning Instance-Specific Augmentations by Capturing Local Invariances"

9 / 9 papers shown

Title
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts Hyunsu Kim Yegon Kim Hongseok Yang Juho Lee 37 0 0 05 Jul 2024
Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning Max Weltevrede Felix Kaubek M. Spaan Wendelin Bohmer 42 0 0 12 Jun 2024
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 60 5 0 13 Mar 2024
A Generative Model of Symmetry Transformations J. Allingham Bruno Mlodozeniec Shreyas Padhy Javier Antorán David Krueger Richard E. Turner Eric T. Nalisnick José Miguel Hernández-Lobato GAN 37 3 0 04 Mar 2024
AROID: Improving Adversarial Robustness through Online Instance-wise Data Augmentation Lin Li Jianing Qiu Michael W. Spratling AAML 30 4 0 12 Jun 2023
Incorporating Unlabelled Data into Bayesian Neural Networks Mrinank Sharma Tom Rainforth Yee Whye Teh Vincent Fortuin SSL UQCV BDL 38 9 0 04 Apr 2023
A General Theory of Correct, Incorrect, and Extrinsic Equivariance Dian Wang Xu Zhu Jung Yeon Park Mingxi Jia Guanang Su Robert W. Platt Robin G. Walters 24 13 0 08 Mar 2023
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity Jang-Hyun Kim Wonho Choo Hosan Jeong Hyun Oh Song 197 176 0 05 Feb 2021
Improving Transformation Invariance in Contrastive Representation Learning Adam Foster Rattana Pukdee Tom Rainforth 51 22 0 19 Oct 2020