34 citations · 96 across the 17 of their papers we have counts for
5 papers · 1 filter
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Qi Tian, Kun Kuang, Kelu Jiang +3
The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet. However, it also means that users' private data may be co…
See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images
Xiaoyan Zhang, Gaoyang Tang, Yingying Zhu +1
The issue of image haze removal has attracted wide attention in recent years. However, most existing haze removal methods cannot restore the scene with clear blue sky, since the co…
SdAE: Self-distillated Masked Autoencoder
Yabo Chen, Yuchen Liu, Dongsheng Jiang +4
With the development of generative-based self-supervised learning (SSL) approaches like BeiT and MAE, how to learn good representations by masking random patches of the input image…
DE-Net: Dynamic Text-guided Image Editing Adversarial Networks
Ming Tao, Bing-Kun Bao, Hao Tang +3
Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (…
DATA: Domain-Aware and Task-Aware Self-supervised Learning
Qing Chang, Junran Peng, Lingxie Xie +4
The paradigm of training models on massive data without label through self-supervised learning (SSL) and finetuning on many downstream tasks has become a trend recently. However, d…