3 citations · 5 across the 15 of their papers we have counts for
7 papers · 1 filter
Medical Image Denosing via Explainable AI Feature Preserving Loss
Guanfang Dong, Anup Basu
Denoising algorithms play a crucial role in medical image processing and analysis. However, classical denoising algorithms often ignore explanatory and critical medical features pr…
Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module
Zijie Tan, Guanfang Dong, Chenqiu Zhao +1
Although Convolutional Neural Networks (CNNs) have achieved promising results in image classification, they still are vulnerable to affine transformations including rotation, trans…
Bridging Distribution Learning and Image Clustering in High-dimensional Space
Guanfang Dong, Chenqiu Zhao, Anup Basu
Distribution learning focuses on learning the probability density function from a set of data samples. In contrast, clustering aims to group similar objects together in an unsuperv…
Learning Distributions via Monte-Carlo Marginalization
Chenqiu Zhao, Guanfang Dong, Anup Basu
We propose a novel method to learn intractable distributions from their samples. The main idea is to use a parametric distribution model, such as a Gaussian Mixture Model (GMM), to…
Is Deep Learning Network Necessary for Image Generation?
Chenqiu Zhao, Guanfang Dong, Anup Basu
Recently, images are considered samples from a high-dimensional distribution, and deep learning has become almost synonymous with image generation. However, is a deep learning netw…
Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks
Chenqiu Zhao, Guanfang Dong, Shupei Zhang +2
Convolutional neural networks have demonstrated impressive results in many computer vision tasks. However, the increasing size of these networks raises concerns about the informati…
Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation
Guanfang Dong, Chenqiu Zhao, Xichen Pan +1
The goal of moving object segmentation is separating moving objects from stationary backgrounds in videos. One major challenge in this problem is how to develop a universal model f…