1 code implementation • 30 Jan 2024 • Chak Fong Chong, Xinyi Fang, Jielong Guo, Yapeng Wang, Wei Ke, Chan-Tong Lam, Sio-Kei Im
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image.
1 code implementation • 29 Dec 2023 • Xiangyu Xiong, Yue Sun, Xiaohong Liu, Wei Ke, Chan-Tong Lam, Jiangang Chen, Mingfeng Jiang, Mingwei Wang, Hui Xie, Tong Tong, Qinquan Gao, Hao Chen, Tao Tan
Experimental results show that DisGAN consistently outperforms the GAN-based augmentation methods with explainable binary classification.
1 code implementation • 24 Nov 2023 • Xiangyu Xiong, Yue Sun, Xiaohong Liu, Chan-Tong Lam, Tong Tong, Hao Chen, Qinquan Gao, Wei Ke, Tao Tan
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets.