research
∙
03/09/2023
Out-of-distribution Detection with Implicit Outlier Transformation
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection...
share
research
∙
09/29/2022
Towards Lightweight Black-Box Attacks against Deep Neural Networks
Black-box attacks can generate adversarial examples without accessing th...
research
∙
04/05/2022
Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders
Hyperspectral imaging has attracted significant attention to identify sp...
research
∙
10/06/2021
SDA-GAN: Unsupervised Image Translation Using Spectral Domain Attention-Guided Generative Adversarial Network
This work introduced a novel GAN architecture for unsupervised image tra...
research
∙
06/15/2021
Probabilistic Margins for Instance Reweighting in Adversarial Training
Reweighting adversarial data during training has been recently shown to ...
research
∙
03/17/2021
Learning with Group Noise
Machine learning in the context of noise is a challenging but practical ...
research
∙
01/14/2021
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
The drastic increase of data quantity often brings the severe decrease o...
research
∙
10/21/2019
A game method for improving the interpretability of convolution neural network
Real artificial intelligence always has been focused on by many machine ...
research
∙
11/21/2018
How to improve the interpretability of kernel learning
In recent years, machine learning researchers have focused on methods to...
research
∙
11/19/2018