3 papers
cs.LG2026
TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
Bowen Liu, Haijian Lai, Chan-Tong Lam +4
Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A recent study proposed a non…
cs.CV2023
Distance Guided Generative Adversarial Network for Explainable Binary Classifications
Xiangyu Xiong, Yue Sun, Xiaohong Liu +10
Despite the potential benefits of data augmentation for mitigating the data insufficiency, traditional augmentation methods primarily rely on the prior intra-domain knowledge. On t…
cs.CV2023
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image Classification
Xiangyu Xiong, Yue Sun, Xiaohong Liu +6
Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative advers…