research
∙
07/14/2023
Omnipotent Adversarial Training for Unknown Label-noisy and Imbalanced Datasets
Adversarial training is an important topic in robust deep learning, but ...
share
research
∙
07/14/2023
Adversarial Training Over Long-Tailed Distribution
In this paper, we study adversarial training on datasets that obey the l...
research
∙
11/24/2022
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels (Short Version)
In this paper, we consider the instance segmentation task on a long-tail...
research
∙
08/05/2022
Low-Light Hyperspectral Image Enhancement
Due to inadequate energy captured by the hyperspectral camera sensor in ...
research
∙
07/11/2022
Privacy-preserving Decentralized Deep Learning with Multiparty Homomorphic Encryption
Decentralized deep learning plays a key role in collaborative model trai...
research
∙
04/07/2022
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks
Multiplication-less neural networks significantly reduce the time and en...
research
∙
08/10/2021
MotionInput v2.0 supporting DirectX: A modular library of open-source gesture-based machine learning and computer vision methods for interacting and controlling existing softwa
Touchless computer interaction has become an important consideration dur...
research
∙
08/02/2020
SCNet: A Neural Network for Automated Side-Channel Attack
The side-channel attack is an attack method based on the information gai...
research
∙
05/06/2020
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
Whereas adversarial training is employed as the main defence strategy ag...
research
∙
05/04/2020
Evaluating Explanation Methods for Neural Machine Translation
Recently many efforts have been devoted to interpreting the black-box NM...
research
∙
04/05/2020
Understanding Learning Dynamics for Neural Machine Translation
Despite the great success of NMT, there still remains a severe challenge...
research
∙
04/05/2020
Detecting and Understanding Generalization Barriers for Neural Machine Translation
Generalization to unseen instances is our eternal pursuit for all data-d...
research
∙
08/24/2018
Approximate Distribution Matching for Sequence-to-Sequence Learning
Sequence-to-Sequence models were introduced to tackle many real-life pro...
research
∙
06/28/2017
