Out-of-Distribution Detection
Detecting samples outside training distribution
Related Topics (Robustness)
Top Papers
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Qihang Zhou, Guansong Pang, Yu Tian et al.
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
Ximiao Zhang, Min Xu, Xiuzhuang Zhou
Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection
Chengjie Wang, wenbing zhu, Bin-Bin Gao et al.
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
Xiaofan Li, Zhizhong Zhang, Xin Tan et al.
Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detection
Zhiwei Yang, Jing Liu, Peng Wu
Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement
Kai Xu, Rongyu Chen, Gianni Franchi et al.
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
Yi-Xin Huang, Hou-I Liu, Hong-Han Shuai et al.
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Tianqi Li, Guansong Pang, wenjun miao et al.
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Yichen Bai, Zongbo Han, Bing Cao et al.
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Irina Rish, Kartik Ahuja, Mohammad Javad Darvishi Bayazi et al.
ExtDM: Distribution Extrapolation Diffusion Model for Video Prediction
Zhicheng Zhang, Junyao Hu, Wentao Cheng et al.
AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP
wenxin ma, Xu Zhang, Qingsong Yao et al.
Spurious Feature Diversification Improves Out-of-distribution Generalization
LIN Yong, Lu Tan, Yifan HAO et al.
Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It
Adam Lilja, Junsheng Fu, Erik Stenborg et al.
NECO: NEural Collapse Based Out-of-distribution detection
Mouïn Ben Ammar, Nacim Belkhir, Sebastian Popescu et al.
GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features
Luc Sträter, Mohammadreza Salehi, Efstratios Gavves et al.
Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning
Wenjun Miao, Guansong Pang, Xiao Bai et al.
Supervised Anomaly Detection for Complex Industrial Images
Aimira Baitieva, David Hurych, Victor Besnier et al.
Exploring Unbiased Deepfake Detection via Token-Level Shuffling and Mixing
Xinghe Fu, Zhiyuan Yan, Taiping Yao et al.
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
Zhen Qu, Xian Tao, Xinyi Gong et al.
Reliability in Semantic Segmentation: Can We Use Synthetic Data?
Thibaut Loiseau, Tuan Hung Vu, Mickael Chen et al.
Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
Guan Gui, Bin-Bin Gao, Jun Liu et al.
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
Qichao Shentu, Beibu Li, Kai Zhao et al.
Long-Tailed Anomaly Detection with Learnable Class Names
Chih-Hui Ho, Kuan-Chuan Peng, Nuno Vasconcelos
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection
Songmin Dai, Yifan Wu, Xiaoqiang Li et al.
LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction
Er Jin, Qihui Feng, Yongli Mou et al.
On the Variance of Neural Network Training with respect to Test Sets and Distributions
Keller Jordan
How to Overcome Curse-of-Dimensionality for Out-of-Distribution Detection?
Soumya Suvra Ghosal, Yiyou Sun, Yixuan Li
GOODAT: Towards Test-Time Graph Out-of-Distribution Detection
Luzhi Wang, Di Jin, He Zhang et al.
Deep Orthogonal Hypersphere Compression for Anomaly Detection
Yunhe Zhang, Yan Sun, Jinyu Cai et al.
Discriminability-Driven Channel Selection for Out-of-Distribution Detection
Yue Yuan, Rundong He, Yicong Dong et al.
Dense Projection for Anomaly Detection
Dazhi Fu, Zhao Zhang, Jicong Fan
A Label-free Heterophily-guided Approach for Unsupervised Graph Fraud Detection
Junjun Pan, Yixin Liu, Xin Zheng et al.
Exploring Diverse Representations for Open Set Recognition
Yu Wang, Junxian Mu, Pengfei Zhu et al.
Playing the Fool: Jailbreaking LLMs and Multimodal LLMs with Out-of-Distribution Strategy
Joonhyun Jeong, Seyun Bae, Yeonsung Jung et al.
Temporally and Distributionally Robust Optimization for Cold-Start Recommendation
Xinyu Lin, Wenjie Wang, Jujia Zhao et al.
EAT: Towards Long-Tailed Out-of-Distribution Detection
Tong Wei, Bo-Lin Wang, Min-Ling Zhang
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation
Min Zhang, Haoxuan Li, Fei Wu et al.
Revisiting Adversarial Training Under Long-Tailed Distributions
Xinli Yue, Ningping Mou, Qian Wang et al.
Unsupervised Layer-Wise Score Aggregation for Textual OOD Detection
Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Camara Gomes et al.
Detecting Out-of-Distribution Through the Lens of Neural Collapse
Litian Liu, Yao Qin
CIFAR-10-Warehouse: Broad and More Realistic Testbeds in Model Generalization Analysis
Xiaoxiao Sun, Xingjian Leng, Zijian Wang et al.
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
Yili Wang, Yixin Liu, Xu Shen et al.
ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open Vocabulary Object Detection
Joonhyun Jeong, Geondo Park, Jayeon Yoo et al.
MetaOOD: Automatic Selection of OOD Detection Models
Yuehan Qin, Yichi Zhang, Yi Nian et al.
Semi-supervised Open-World Object Detection
Sahal Shaji Mullappilly, Abhishek Singh Gehlot, Rao Muhammad Anwer et al.
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
Xiaolei Wang, Xiaoyang Wang, Huihui Bai et al.
Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
Fenfang Tao, Guo-Sen Xie, Fang Zhao et al.
What How and When Should Object Detectors Update in Continually Changing Test Domains?
Jayeon Yoo, Dongkwan Lee, Inseop Chung et al.
A Noisy Elephant in the Room: Is Your Out-of-Distribution Detector Robust to Label Noise?
Galadrielle Humblot-Renaux, Sergio Escalera, Thomas B. Moeslund
PixOOD: Pixel-Level Out-of-Distribution Detection
Tomas Vojir, Jan Sochman, Jiri Matas
SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch
Chun-Liang Li, Tomas Pfister, Kihyuk Sohn et al.
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
Yu Yongcan, Lijun Sheng, Ran He et al.
Just a Hint: Point-Supervised Camouflaged Object Detection
Huafeng Chen, Dian SHAO, Guangqian Guo et al.
How Contaminated Is Your Benchmark? Measuring Dataset Leakage in Large Language Models with Kernel Divergence
Hyeong Kyu Choi, Maxim Khanov, Hongxin Wei et al.
GDA: Generalized Diffusion for Robust Test-time Adaptation
Yun-Yun Tsai, Fu-Chen Chen, Albert Chen et al.
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
Shun Wei, Jielin Jiang, Xiaolong Xu
Can OOD Object Detectors Learn from Foundation Models?
Jiahui Liu, Xin Wen, Shizhen Zhao et al.
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains
Eunsu Baek, Keondo Park, Ji-yoon Kim et al.
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
Jiahao Xu, Zikai Zhang, Rui Hu
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
Zihan Guan, Mengxuan Hu, Ronghang Zhu et al.
CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning
Hyuck Lee, Heeyoung Kim
Deep Kernel Relative Test for Machine-generated Text Detection
Yiliao Song, Zhenqiao Yuan, Shuhai Zhang et al.
DOTA: Distributional Test-time Adaptation of Vision-Language Models
Zongbo Han, Jialong Yang, Guangyu Wang et al.
UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks
Yuanbin Qian, Shuhan Ye, Chong Wang et al.
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection
Alon Zolfi, Guy AmiT, Amit Baras et al.
Learning Diffusion Models for Multi-View Anomaly Detection
Chieh Liu, Yu-Min Chu, Ting-I Hsieh et al.
Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers
Kiyoung Seong, Seonghyun Park, Seonghwan Kim et al.
Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings
Hossein Mirzaei Sadeghlou, Mackenzie Mathis
Data Taggants: Dataset Ownership Verification Via Harmless Targeted Data Poisoning
Wassim Bouaziz, Nicolas Usunier, El-Mahdi El-Mhamdi
On the Limitations of Temperature Scaling for Distributions with Overlaps
Muthu Chidambaram, Rong Ge
Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection
Zining Chen, Xingshuang Luo, Weiqiu Wang et al.
Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
Fanhu Zeng, Zhen Cheng, Fei Zhu et al.
Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks
Danni Yuan, Mingda Zhang, Shaokui Wei et al.
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts
Youssef Mansour, Xuyang Zhong, Serdar Caglar et al.
Robustness Auditing for Linear Regression: To Singularity and Beyond
Ittai Rubinstein, Samuel Hopkins
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
Yingwen Wu, Ruiji Yu, Xinwen Cheng et al.
Weak Distribution Detectors Lead to Stronger Generalizability of Vision-Language Prompt Tuning
Kun Ding, Haojian Zhang, Qiang Yu et al.
Assessing Pre-Trained Models for Transfer Learning Through Distribution of Spectral Components
Tengxue Zhang, Yang Shu, Xinyang Chen et al.
DRL: Decomposed Representation Learning for Tabular Anomaly Detection
Hangting Ye, He Zhao, Wei Fan et al.
Out-of-Variable Generalisation for Discriminative Models
Siyuan Guo, Jonas Wildberger, Bernhard Schoelkopf
Joint Out-of-Distribution Filtering and Data Discovery Active Learning
Sebastian Schmidt, Leonard Schenk, Leo Schwinn et al.
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
Ziming Huang, Xurui Li, Haotian Liu et al.
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
Yuhao Yi, Ronghui You, Hong Liu et al.
Difficulty-aware Balancing Margin Loss for Long-tailed Recognition
Minseok Son, Inyong Koo, Jinyoung Park et al.
SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning
ZERUN WANG, Liuyu Xiang, Lang Huang et al.
Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAV Target Detection
Houzhang Fang, Xiaolin Wang, Zengyang Li et al.
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
Alexey Nekrasov, Malcolm Burdorf, Stewart Worrall et al.
Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation
Mohit Prashant, Arvind Easwaran, Suman Das et al.
ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection
Erik Wallin, Lennart Svensson, Fredrik Kahl et al.
TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
Jiankang Chen, Tong Zhang, Wei-shi Zheng et al.
ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks
Renshuai Tao, Manyi Le, Chuangchuang Tan et al.
Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
Bowen Liu, Haoyang Li, Shuning Wang et al.
Disentangling Tabular Data Towards Better One-Class Anomaly Detection
Jianan Ye, Zhaorui Tan, Yijie Hu et al.
PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection
Wei Li, Pin-Yu Chen, Sijia Liu et al.
Tight Rates in Supervised Outlier Transfer Learning
Mohammadreza Mousavi Kalan, Samory Kpotufe
DF-MIA: A Distribution-Free Membership Inference Attack on Fine-Tuned Large Language Models
Zhiheng Huang, Yannan Liu, Daojing He et al.
Simplification Is All You Need against Out-of-Distribution Overconfidence
Keke Tang, Chao Hou, Weilong Peng et al.
Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection
Meng Xing, Zhiyong Feng, Yong Su et al.
Lie Detector: Unified Backdoor Detection via Cross-Examination Framework
Xuan Wang, Siyuan Liang, Dongping Liao et al.