25 citations · 31 across the 3 of their papers we have counts for
7 papers
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
Chunhui Zhang, Yixiong Chen, Li Liu +2
The self-supervised ultrasound (US) video model pretraining can use a small amount of labeled data to achieve one of the most promising results on US diagnosis. However, it does no…
On the Number of Linear Regions of Convolutional Neural Networks
H. Xiong, L. Huang, M. Yu +3
One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that…
Embarrassingly Simple Binary Representation Learning
Yuming Shen, Jie Qin, Jiaxin Chen +2
Recent binary representation learning models usually require sophisticated binary optimization, similarity measure or even generative models as auxiliaries. However, one may wonder…
Noisy-As-Clean: Learning Self-supervised Denoising from the Corrupted Image
Jun Xu, Yuan Huang, Ming-Ming Cheng +4
Supervised deep networks have achieved promisingperformance on image denoising, by learning image priors andnoise statistics on plenty pairs of noisy and clean images. Unsupervised…
NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
Yingkun Hou, Jun Xu, Mingxia Liu +4
Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS pri…
STAR: A Structure and Texture Aware Retinex Model
Jun Xu, Yingkun Hou, Dongwei Ren +5
Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives a…