Search Results for author: Qizhou Wang

Found 19 papers, 9 papers with code

Do CLIPs Always Generalize Better than ImageNet Models?

no code implementations18 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.

Data Mixture in Training Un-assures Out-of-Distribution Generalization

no code implementations25 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.

Data Augmentation Out-of-Distribution Generalization

Artificial intelligence optical hardware empowers high-resolution hyperspectral video understanding at 1.2 Tb/s

no code implementations17 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.

Semantic Segmentation Video Semantic Segmentation +1

Open-Set Graph Anomaly Detection via Normal Structure Regularisation

no code implementations12 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.

Graph Anomaly Detection Supervised Anomaly Detection

Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources

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

Learning to Augment Distributions for Out-of-Distribution Detection

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.

Learning Theory Out-of-Distribution Detection

Out-of-distribution Detection with Implicit Outlier Transformation

1 code implementation9 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.

Out-of-Distribution Detection

Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment

1 code implementation2 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.

Contrastive Learning Domain Adaptation +1

Watermarking for Out-of-distribution Detection

1 code implementation27 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.

Out-of-Distribution Detection

Towards Lightweight Black-Box Attacks against Deep Neural Networks

1 code implementation29 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.

Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders

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.

Image Classification Semantic Segmentation +1

Probabilistic Margins for Instance Reweighting in Adversarial Training

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.

Adversarial Robustness

Learning with Group Noise

no code implementations17 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.

Learning with noisy labels Relation

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

1 code implementation14 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).

A self-explanatory method for the black problem on discrimination part of CNN

no code implementations1 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.

How to improve the interpretability of kernel learning

no code implementations21 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.

BIG-bench Machine Learning

How far from automatically interpreting deep learning

no code implementations19 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.

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