no code implementations • 5 Feb 2024 • Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao
We prove that with the introduction of a pre-trained source-only domain discriminator, the empirical estimation error of $\gH$-divergence related adversarial loss is reduced from the source domain side.
no code implementations • 28 Jul 2022 • Chunwei Ma, Zhanghexuan Ji, Ziyun Huang, Yan Shen, Mingchen Gao, Jinhui Xu
Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs).
no code implementations • 5 May 2022 • Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao
Based on our particle filter inference algorithm, a semi-supervised learn-ing algorithm is utilized for learning tracking network on intermittent labeled frames by variational inference.
1 code implementation • 5 Feb 2022 • Chunwei Ma, Ziyun Huang, Mingchen Gao, Jinhui Xu
One observation is that the widely embraced ProtoNet model is essentially a Voronoi Diagram (VD) in the feature space.
no code implementations • ICLR 2022 • Chunwei Ma, Ziyun Huang, Mingchen Gao, Jinhui Xu
One observation is that the widely embraced ProtoNet model is essentially a Dirichlet Tessellation (Voronoi Diagram) in the feature space.
1 code implementation • 25 Jun 2021 • Chunwei Ma, Ziyun Huang, Jiayi Xian, Mingchen Gao, Jinhui Xu
Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their learning process.
no code implementations • 1 Jan 2021 • Yan Shen, Jian Du, Chunwei Ma, Mingchen Gao, Benyu Zhang
Our introduced SGLD oracle would lower generalization errors in local node's parameter learning and provide local node DP protections.
no code implementations • 5 Nov 2019 • Zhanghexuan Ji, Yan Shen, Chunwei Ma, Mingchen Gao
In this paper, we use only two kinds of weak labels, i. e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions.
1 code implementation • 20 Sep 2019 • Chunwei Ma, Zhanghexuan Ji, Mingchen Gao
Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment.
Ranked #2 on Cardiovascular MR Segmentaiton on HVSMR 2016