no code implementations • 18 Mar 2024 • Qizhou Wang, Yong Lin, Yongqiang Chen, Ludwig Schmidt, Bo Han, Tong Zhang
The performance drops from the common to counter groups quantify the reliance of models on spurious features (i. e., backgrounds) to predict the animals.
no code implementations • 25 Dec 2023 • Songming Zhang, Yuxiao Luo, Qizhou Wang, Haoang Chi, Weikai Li, Bo Han, Jinyan Li
We study the problem of out-of-distribution (OOD) generalization capability of models by exploring the relationship between generalization error and training set size.
no code implementations • 17 Dec 2023 • Maksim Makarenko, Qizhou Wang, Arturo Burguete-Lopez, Silvio Giancola, Bernard Ghanem, Luca Passone, Andrea Fratalocchi
The technology platform combines artificial intelligence hardware, processing information optically, with state-of-the-art machine vision networks, resulting in a data processing speed of 1. 2 Tb/s with hundreds of frequency bands and megapixel spatial resolution at video rates.
no code implementations • 12 Nov 2023 • Qizhou Wang, Guansong Pang, Mahsa Salehi, Christopher Leckie
However, current methods tend to over-emphasise fitting the seen anomalies, leading to a weak generalisation ability to detect unseen anomalies, i. e., those that are not illustrated by the labelled anomaly nodes.
1 code implementation • NeurIPS 2023 • Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han
To this end, we suggest that generated data (with mistaken OOD generation) can be used to devise an auxiliary OOD detection task to facilitate real OOD detection.
Out-of-Distribution Detection Out of Distribution (OOD) Detection +1
1 code implementation • NeurIPS 2023 • Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han
Accordingly, we propose Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution discrepancy by crafting an OOD distribution set that contains all distributions in a Wasserstein ball centered on the auxiliary OOD distribution.
1 code implementation • 9 Mar 2023 • Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han
It leads to a min-max learning scheme -- searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for uniform performance in OOD detection.
Ranked #12 on Out-of-Distribution Detection on ImageNet-1k vs Textures
1 code implementation • 2 Dec 2022 • Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie
In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions.
1 code implementation • 27 Oct 2022 • Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han
Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models.
Ranked #20 on Out-of-Distribution Detection on ImageNet-1k vs Places
1 code implementation • 29 Sep 2022 • Chenghao Sun, Yonggang Zhang, Wan Chaoqun, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian
As it is hard to mitigate the approximation error with few available samples, we propose Error TransFormer (ETF) for lightweight attacks.
1 code implementation • CVPR 2022 • Maksim Makarenko, Arturo Burguete-Lopez, Qizhou Wang, Fedor Getman, Silvio Giancola, Bernard Ghanem, Andrea Fratalocchi
Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision.
no code implementations • 6 Oct 2021 • Qizhou Wang, Maksim Makarenko
This work introduced a novel GAN architecture for unsupervised image translation on the task of face style transform.
1 code implementation • NeurIPS 2021 • Qizhou Wang, Feng Liu, Bo Han, Tongliang Liu, Chen Gong, Gang Niu, Mingyuan Zhou, Masashi Sugiyama
Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights.
no code implementations • 17 Mar 2021 • Qizhou Wang, Jiangchao Yao, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han
Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels.
1 code implementation • 14 Jan 2021 • Qizhou Wang, Bo Han, Tongliang Liu, Gang Niu, Jian Yang, Chen Gong
The drastic increase of data quantity often brings the severe decrease of data quality, such as incorrect label annotations, which poses a great challenge for robustly training Deep Neural Networks (DNNs).
no code implementations • 1 Jan 2021 • Jinwei Zhao, Qizhou Wang, Wanli Qiu, Guo Xie, Wei Wang, Xinhong Hei, Deyu Meng
However, it is hard for the interpretable models to approximate the discrimination part because of the tradeoff problem between interpretability performance and generalization performance of the discrimination part.
no code implementations • 21 Oct 2019 • Jinwei Zhao, Qizhou Wang, Fuqiang Zhang, Wanli Qiu, YuFei Wang, Yu Liu, Guo Xie, Weigang Ma, Bin Wang, Xinhong Hei
The reason is, we believe that: the network is essentially a perceptual model.
no code implementations • 21 Nov 2018 • Jinwei Zhao, Qizhou Wang, YuFei Wang, Yu Liu, Zhenghao Shi, Xinhong Hei
In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed.
no code implementations • 19 Nov 2018 • Jinwei Zhao, Qizhou Wang, YuFei Wang, Xinhong Hei, Yu Liu
In other words, there is a gap between the deep learning model and the cognitive mode.