109 citations · 288 across the 29 of their papers we have counts for
30 papers
Optical Flow for Video Super-Resolution: A Survey
Zhigang Tu, Hongyan Li, Wei Xie +4
Video super-resolution is currently one of the most active research topics in computer vision as it plays an important role in many visual applications. Generally, video super-reso…
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos
Nupur Thakur, Baoxin Li
Extensive research has demonstrated that deep neural networks (DNNs) are prone to adversarial attacks. Although various defense mechanisms have been proposed for image classificati…
FedNS: Improving Federated Learning for collaborative image classification on mobile clients
Yaoxin Zhuo, Baoxin Li
Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server. The most popu…
Partial Domain Adaptation Using Selective Representation Learning For Class-Weight Computation
Sandipan Choudhuri, Riti Paul, Arunabha Sen +2
The generalization power of deep-learning models is dependent on rich-labelled data. This supervision using large-scaled annotated information is restrictive in most real-world sce…
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
Yuzhen Ding, Nupur Thakur, Baoxin Li
Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversarial images are created to trick a network into misclassification even if the imag…
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks
Nupur Thakur, Yuzhen Ding, Baoxin Li
Though deep neural networks (DNNs) have shown superiority over other techniques in major fields like computer vision, natural language processing, robotics, recently, it has been p…