Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

30 March 2021

Papers citing "Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training"

8 / 8 papers shown

Title
Automated data processing and feature engineering for deep learning and big data applications: a survey A. Mumuni F. Mumuni TPM 48 49 0 18 Mar 2024
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods A. Mumuni F. Mumuni 70 5 0 13 Mar 2024
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations Tonmoy Hossain Miaomiao Zhang 36 2 0 20 Dec 2023
Data Scarcity in Recommendation Systems: A Survey Zefeng Chen Wensheng Gan Jiayang Wu Kaixia Hu Hong Lin 36 21 0 08 Dec 2023
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images Mokshagna Sai Teja Karanam Tushar Kataria Krithika S. Iyer Shireen Y. Elhabian MedIm 21 4 0 06 Jul 2023
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation Yunhe Gao Zhuowei Li Di Liu Mu Zhou Shaoting Zhang Dimitris N. Metaxas MedIm 31 12 0 04 Jun 2023
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification Tian Xia Pedro Sanchez C. Qin Sotirios A. Tsaftaris OOD MedIm 29 12 0 15 Mar 2022
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation Chen Chen C. Qin Cheng Ouyang Zeju Li Shuo Wang Huaqi Qiu Liang Chen G. Tarroni Wenjia Bai Daniel Rueckert GAN MedIm 62 40 0 07 Aug 2021