OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

1 code implementation • 15 Jun 2023 • Jingyang Zhang, Jingkang Yang, Pengyun Wang, Haoqi Wang, Yueqian Lin, Haoran Zhang, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Yixuan Li, Ziwei Liu, Yiran Chen, Hai Li

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Generative Oversampling for Imbalanced Data via Majority-Guided VAE

no code implementations • 14 Feb 2023 • Qingzhong Ai, Pengyun Wang, Lirong He, Liangjian Wen, Lujia Pan, Zenglin Xu

Learning with imbalanced data is a challenging problem in deep learning.

Ti-MAE: Self-Supervised Masked Time Series Autoencoders

1 code implementation • 21 Jan 2023 • Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, Zenglin Xu

Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.

Contrastive Learning Multivariate Time Series Forecasting +2

OpenOOD: Benchmarking Generalized Out-of-Distribution Detection

3 code implementations • 13 Oct 2022 • Jingkang Yang, Pengyun Wang, Dejian Zou, Zitang Zhou, Kunyuan Ding, Wenxuan Peng, Haoqi Wang, Guangyao Chen, Bo Li, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, Dan Hendrycks, Yixuan Li, Ziwei Liu

Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.

Anomaly Detection Benchmarking +3

Multi-relation Message Passing for Multi-label Text Classification

1 code implementation • 10 Feb 2022 • Muberra Ozmen, Hao Zhang, Pengyun Wang, Mark Coates

These examples motivate the modelling of multiple types of bi-directional relationships between labels.

Multi-Label Classification Multi-Label Image Classification +4

Label-Aware Distribution Calibration for Long-tailed Classification

no code implementations • 9 Nov 2021 • Chaozheng Wang, Shuzheng Gao, Cuiyun Gao, Pengyun Wang, Wenjie Pei, Lujia Pan, Zenglin Xu

Real-world data usually present long-tailed distributions.

Classification

Mask-GVAE: Blind Denoising Graphs via Partition

1 code implementation • 8 Feb 2021 • Jia Li, Mengzhou Liu, Honglei Zhang, Pengyun Wang, Yong Wen, Lujia Pan, Hong Cheng

We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.

Denoising

Predicting Path Failure In Time-Evolving Graphs

2 code implementations • 10 May 2019 • Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan

Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.