Regularising for invariance to data augmentation improves supervised learning

7 March 2022

Papers citing "Regularising for invariance to data augmentation improves supervised learning"

9 / 9 papers shown

Title
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets Marc Uecker J. M. Zöllner 20 0 0 27 Sep 2024
Generating Physical Dynamics under Priors Zihan Zhou Xiaoxue Wang Tianshu Yu DiffM AI4CE 48 0 0 01 Sep 2024
Understanding the Detrimental Class-level Effects of Data Augmentation Polina Kirichenko Mark Ibrahim Randall Balestriero Diane Bouchacourt Ramakrishna Vedantam Hamed Firooz Andrew Gordon Wilson 22 12 0 07 Dec 2023
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning Khanh-Binh Nguyen 8 3 0 24 Oct 2023
Contextual Reliability: When Different Features Matter in Different Contexts Gaurav R. Ghosal Amrith Rajagopal Setlur Daniel S. Brown Anca Dragan Aditi Raghunathan 17 1 0 19 Jul 2023
Subspace-Configurable Networks Dong Wang O. Saukh Xiaoxi He Lothar Thiele OOD 20 0 0 22 May 2023
Image augmentation with conformal mappings for a convolutional neural network O. Rainio Mohamed M. S. Nasser M. Vuorinen R. Klén 11 3 0 10 Dec 2022
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning Guozheng Ma Zhen Wang Zhecheng Yuan Xueqian Wang Bo Yuan Dacheng Tao OffRL 18 25 0 10 Oct 2022
High-Performance Large-Scale Image Recognition Without Normalization Andrew Brock Soham De Samuel L. Smith Karen Simonyan VLM 220 450 0 11 Feb 2021