117 citations · 160 across the 7 of their papers we have counts for
12 papers
Transferred Discrepancy: Quantifying the Difference Between Representations
Yunzhen Feng, Runtian Zhai, Di He +2
Understanding what information neural networks capture is an essential problem in deep learning, and studying whether different models capture similar features is an initial step t…
RODE-Net: Learning Ordinary Differential Equations with Randomness from Data
Junyu Liu, Zichao Long, Ranran Wang +2
Random ordinary differential equations (RODEs), i.e. ODEs with random parameters, are often used to model complex dynamics. Most existing methods to identify unknown governing RODE…
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Haimiao Zhang, Baodong Liu, Hengyong Yu +1
X-ray Computed Tomography (CT) is widely used in clinical applications such as diagnosis and image-guided interventions. In this paper, we propose a new deep learning based model f…
Blind Adversarial Training: Balance Accuracy and Robustness
Haidong Xie, Xueshuang Xiang, Naijin Liu +1
Adversarial training (AT) aims to improve the robustness of deep learning models by mixing clean data and adversarial examples (AEs). Most existing AT approaches can be grouped int…
Distillation Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network
Bin Dong, Jikai Hou, Yiping Lu +1
Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical u…
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Fei Yu, Jie Zhao, Yanjun Gong +6
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual ann…