13 citations · 14 across the 2 of their papers we have counts for
6 papers
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
Ximei Wang, Xinyang Chen, Jianmin Wang +1
To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classi…
Regressive Domain Adaptation for Unsupervised Keypoint Detection
Junguang Jiang, Yifei Ji, Ximei Wang +3
Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of…
Self-Tuning for Data-Efficient Deep Learning
Ximei Wang, Jinghan Gao, Mingsheng Long +1
Deep learning has made revolutionary advances to diverse applications in the presence of large-scale labeled datasets. However, it is prohibitively time-costly and labor-expensive…
Bi-tuning of Pre-trained Representations
Jincheng Zhong, Ximei Wang, Zhi Kou +2
It is common within the deep learning community to first pre-train a deep neural network from a large-scale dataset and then fine-tune the pre-trained model to a specific downstrea…
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Ximei Wang, Mingsheng Long, Jianmin Wang +1
Domain Adaptation (DA) enables transferring a learning machine from a labeled source domain to an unlabeled target one. While remarkable advances have been made, most of the existi…
Minimum Class Confusion for Versatile Domain Adaptation
Ying Jin, Ximei Wang, Mingsheng Long +1
There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-…