DHA: End-to-End Joint Optimization of Data Augmentation Policy,
Hyper-parameter and Architecture

DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture

13 September 2021

Lanqing Hong

Papers citing "DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture"

9 / 9 papers shown

Title
FreeAugment: Data Augmentation Search Across All Degrees of Freedom Tom Bekor Niv Nayman Lihi Zelnik-Manor ViT 36 0 0 07 Sep 2024
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 60 4 0 13 Mar 2024
LatentAugment: Dynamically Optimized Latent Probabilities of Data Augmentation K. Kuriyama 19 1 0 04 May 2023
Neural Architecture Search: Insights from 1000 Papers Colin White Mahmoud Safari R. Sukthanker Binxin Ru T. Elsken Arber Zela Debadeepta Dey Frank Hutter 3DV AI4CE 32 128 0 20 Jan 2023
DAAS: Differentiable Architecture and Augmentation Policy Search Xiaoxing Wang Xiangxiang Chu Junchi Yan Xiaokang Yang 14 5 0 30 Sep 2021
Learning Deep Morphological Networks with Neural Architecture Search Yufei Hu Nacim Belkhir Jesús Angulo Angela Yao Gianni Franchi AI4CE 20 20 0 14 Jun 2021
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution T. Elsken J. H. Metzen Frank Hutter 117 498 0 24 Apr 2018
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,326 0 05 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,886 0 15 Sep 2016