MetaAugment: Sample-Aware Data Augmentation Policy Learning

MetaAugment: Sample-Aware Data Augmentation Policy Learning

22 December 2020

Lanqing Hong

Papers citing "MetaAugment: Sample-Aware Data Augmentation Policy Learning"

10 / 10 papers shown

Title
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification Guanyi Mou Yichuan Li Kyumin Lee 28 3 0 26 Sep 2024
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
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 65 5 0 13 Mar 2024
LatentAugment: Dynamically Optimized Latent Probabilities of Data Augmentation K. Kuriyama 24 1 0 04 May 2023
LA3: Efficient Label-Aware AutoAugment Mingjun Zhao Sha Lu Zixuan Wang Xiaoli Wang Di Niu 11 1 0 20 Apr 2023
Automatic Data Augmentation via Invariance-Constrained Learning Ignacio Hounie Luiz F. O. Chamon Alejandro Ribeiro 23 10 0 29 Sep 2022
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture Kaichen Zhou Lanqing Hong Shuailiang Hu Fengwei Zhou Binxin Ru Jiashi Feng Zhenguo Li 54 10 0 13 Sep 2021
An overview of mixing augmentation methods and augmentation strategies Dominik Lewy Jacek Mañdziuk 23 59 0 21 Jul 2021
Direct Differentiable Augmentation Search Aoming Liu Zehao Huang Zhiwu Huang Naiyan Wang 25 32 0 09 Apr 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 323 11,681 0 09 Mar 2017