Data Contamination

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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Data Leakage in Patch-Based Hyperspectral Image Classification: Quantifying the Impact of Spatial Overlap
Oct 6, 2026Mohammed Q. AlkhatibData Contamination Hyperspectral Image Classification
College of Engineering and IT, University of Dubai, Dubai, 14143, UAE
Prevalence Determines Precision:Silent Contamination in Detector-Defined Datasets
Sep 12, 2026Jia Huang, Yankai Wan, Yangjun OuBenchmark Validity Data Contamination
Guanghua School of Management, Peking University · College of Artificial Intelligence, Jilin University · School of Mathematical Sciences, Peking University
A Later Test Set Is Not a New Domain: Pretraining Familiarity Survives a Contamination-Free Hold-Out
Sep 9, 2026Mahdi Naser Moghadasi, Faezeh GhaderiZero-Shot Time Series Forecasting Domain Generalization
BrightMind AI · University of Texas at Arlington
Ten Architectures, One Error: Shared Failure Modes in Hyperspectral Classification under Spatially Disjoint Evaluation
Sep 1, 2026Ehsan Faghih, Fatemeh Ashrafi, Marguerite Moore +1Data Contamination Remote Sensing Benchmarks
North Carolina State University, Wilson College of Textiles Raleigh, NC, USA
Auditing Chinese Web-scale Corpora via Sampled BPE Token Statistics
Aug 11, 2026Qingjie Zhang, Ziqi Tang, Jie Zhang +7LLM Auditing Data Contamination
Tsinghua University · SiliconProspect AI · Nanyang Technological University +1
Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
Jul 29, 2026Seunghun Yu, Meiyi Zhu, Petar Popovski +2Conformal Prediction Change-Point Detection
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea · Department of Engineering, King’s College London, WC2R 2LS, London, U.K. · Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark +2
Adversarial Contamination Meets Hard Thresholding: An Iterative Algorithm with Signal Adaptivity and Minimax Optimality
Jun 26, 2026Shixiang Liu, Hanming YangRobust Regression Sparse Recovery
School of Statistics, Renmin University of China · Institute of Statistics and Big Data, Renmin University of China
Computationally tractable robust differentially private mean estimation
Jun 10, 2026Kelly RamsayDifferential Privacy Data Contamination
Department of Mathematics and Statistics York University Toronto, ON, Canada
Ethical and Technical Limits of Deepfake Speech Datasets
Jun 9, 2026Vojtěch Staněk, Eva Trnovská, Kamil Malinka +1Data Leakage Audio Deepfake Detection
Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data
May 29, 2026Matthias TemplData Contamination
School of Business, FHNW Fachhochschule Nordwestschweiz, Riggenbachstrasse 16, 4600 Olten, Switzerland
No Safe Dose: How Training Data Drives Unsafe Image Generation
May 27, 2026Felix Friedrich, Lukas Helff, Niharika Hegde +2Safe T2I Generation Data Contamination
1Black Forest Labs · 2TU Darmstadt & hessian.AI · 3DFKI +1
CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities
May 25, 2026Junyuan Liu, Xinglei Wang, Zichao Zeng +5Representation Learning Geospatial Representation Learning
SpaceTimeLab, University College London, UK · 3DIMPact, University College London, UK · School of Resource and Environmental Sciences, Wuhan University, China +1
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
May 24, 2026Hongkai Li, Shifeng Xie, Lefei Shen +7Data Contamination Time-Series Foundation Models
Zhejiang University · Télécom Paris · State Street Technology (Zhejiang) Ltd. +1
Spiking the training data to correct for test set contamination
May 24, 2026Johnny Tian-Zheng Wei, Jerry Li, Ameya Godbole +1Post-Hoc Calibration Benchmark Contamination
University of Southern California
On the Sample Complexity of Robust Binary Hypothesis Testing
May 23, 2026Shankar Vallinayagam, Ankit Pensia, Varun JogSample Complexity Data Contamination
Department of Pure Mathematics and Mathematical Statistics, University of Cambridge · Department of Statistics, Carnegie Mellon University
Physics-R1: An Audited Olympiad Corpus and Recipe for Visual Physics Reasoning
May 13, 2026Shan YangVisual Reasoning RL for Language Model Reasoning
Independent Researcher
The frame-level leakage trap: rethinking evaluation protocols for intrinsic image decomposition, with source-separable uncertainty as a case study
May 7, 2026Jihwan WooData Leakage Intrinsic Image Decomposition
Amazon Web Services (AWS), Partner Field APJ, Sr. Specialist Solutions Architect, AI/ML
When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contamination
May 7, 2026Congye WangConformal Prediction Data Contamination
Newcastle University
SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination
Apr 30, 2026Eichi UeharaDouble ML Robust Regression
Independent researcher.
High-Dimensional Gaussian Mean Estimation under Realizable Contamination
Mar 17, 2026Ilias Diakonikolas, Daniel M. Kane, Thanasis PittasData Contamination Statistical Learning Theory
University of Wisconsin-Madison · University of California, San Diego
Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks
Feb 10, 2026David S. Duque-Castaño, Lauren Flor-Torres, Jorge I. ZuluagaDenoising Autoencoders Autoencoders
SEAP/FACom, Instituto de Física - FCEN, Universidad de Antioquia, Calle 70 No. 52-21, Medellín, Colombia
Are Whitepaper Claims Reflected in Market Structure? A Contamination-Aware Pipeline and a Power-Limited Null
Jan 28, 2026Murad FarzullaData Contamination Representation Alignment
Dissensus, London, UK · King’s College London, London, UK
When Auditors Fabricate: Batch-Size Degradation and Confident Hallucination in LLM Detection of Planted Document Contamination
Date pendingKaran Parekh, Sanjana Pendyala Ravinder, Sana Mhapsekar +1LLM Auditing Data Contamination
University of North Texas

Oct 6, 2026, cs.CVJ/K move · Enter open · S save

Data Leakage in Patch-Based Hyperspectral Image Classification: Quantifying the Impact of Spatial Overlap

Mohammed Q. Alkhatib

College of Engineering and IT, University of Dubai, Dubai, 14143, UAE

Data Contamination Hyperspectral Image Classification Image Classification

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Patch-based learning improves hyperspectral image (HSI) classification by exploiting local spectral-spatial information, but random train-test sampling from the same image can cause spatial patch overlap, leading to data leakage and optimistic performance estimates. This paper investigates same-class train-test spatial overlap in patch-based HSI classification using two measures: overlap percentage (OP), which quantifies the global amount of overlapped testing patch pixels, and average overlap ratio (AOR), which measures the local severity among affected testing patches. Experiments on the Pavia University dataset compare random and non-random spatial sampling using SVM, MLP, 2D-CNN, 3D-CNN, ViT, and MorpMamba. The results show that deep patch-based models achieve high accuracy under random sampling, with 3D-CNN reaching 96.17% Overall Accuracy (OA), but drop substantially under non-random spatial sampling, where 3D-CNN decreases to 55.20% and ViT and 2D-CNN drop by 40.71 and 38.81 percentage points (PP), respectively. Patch-size analysis further shows that increasing the patch size from 5x5 to 19x19 raises the random-sampling overlap percentage from 23.28% to 77.02%. These findings demonstrate that random patch-based evaluation can substantially inflate classification performance, especially for models that strongly exploit spatial context. The code associated with this paper is available at: https://github.com/mqalkhatib/Data_Leakage_in_HSI_Classification.

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