Further advantages of data augmentation on convolutional neural networks

Further advantages of data augmentation on convolutional neural networks

26 June 2019

Alex Hernández-García

Papers citing "Further advantages of data augmentation on convolutional neural networks"

7 / 7 papers shown

Title
Image Recognition for Garbage Classification Based on Pixel Distribution Learning Jenil Kanani 23 0 0 05 Sep 2024
Steerable Equivariant Representation Learning Sangnie Bhardwaj Willie McClinton Tongzhou Wang Guillaume Lajoie Chen Sun Phillip Isola Dilip Krishnan OOD LLMSV 34 5 0 22 Feb 2023
How Much Data Are Augmentations Worth? An Investigation into Scaling Laws, Invariance, and Implicit Regularization Jonas Geiping Micah Goldblum Gowthami Somepalli Ravid Shwartz-Ziv Tom Goldstein A. Wilson 26 35 0 12 Oct 2022
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments Randall Balestriero Ishan Misra Yann LeCun 35 20 0 16 Feb 2022
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization Xiaojun Xu Jacky Y. Zhang Evelyn Ma Danny Son Oluwasanmi Koyejo Bo-wen Li 20 10 0 03 Feb 2022
Randomness In Neural Network Training: Characterizing The Impact of Tooling Donglin Zhuang Xingyao Zhang Shuaiwen Leon Song Sara Hooker 25 75 0 22 Jun 2021
Data augmentation instead of explicit regularization Alex Hernández-García Peter König 32 141 0 11 Jun 2018