most citedAutomatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

4 citations · 7 across the 2 of their papers we have counts for

collaborators

3 papers

cs.CV2020

Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation

Tiexin Qin, Wenbin Li, Yinghuan Shi +1

Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current wor…

eess.IV2020★ 4 cited

Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation

Tiexin Qin, Ziyuan Wang, Kelei He +3

Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance…

cs.CV2019★ 3 cited

Automatic Data Augmentation by Learning the Deterministic Policy

Yinghuan Shi, Tiexin Qin, Yong Liu +3

Aiming to produce sufficient and diverse training samples, data augmentation has been demonstrated for its effectiveness in training deep models. Regarding that the criterion of th…