Distribution Shift Detection

Follow this topic Ask about this topic

Momentum

5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 36

All topics

LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
Apr 28, 2026James Flora, Kowshik Thopalli, Akshay R. Kulkarni +2Distribution Shift Distribution Shift Detection
School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA · Lawrence Livermore National Laboratory, Livermore, California, USA · University of California San Diego, California, USA
Information-Geometric First-Passage Monitoring of Distributional Stability in Stochastic Systems
Apr 27, 2026Hikmat Karimov, Rahid Zahid AlekberliInformation Geometry Distribution Shift
Institute of Defense Technologies and Cybersecurity„ Azerbaijan Technical University, Azerbaijan
Component-Based Out-of-Distribution Detection
Apr 23, 2026Wenrui Liu, Hong Chang, Ruibing Hou +2Distribution Shift Detection OOD Detection
The State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China · The University of Chinese Academy of Sciences, Beijing 100049, China
In-Context Learning Under Regime Change
Apr 18, 2026Carson Dudley, Yutong Bi, Xiaofeng Liu +1Transformer Distribution Shift Detection
Department of Mathematics, University of Michigan · Department of Electrical Engineering and Computer Science, University of Michigan · Michigan Institute for AI and Data in Society, University of Michigan
OT on the Map: Quantifying Domain Shifts in Geographic Space
Apr 17, 2026Haoran Zhang, Livia Betti, Konstantin Klemmer +2Distribution Shift Geospatial Representation Learning
Harvard University · University of Colorado Boulder · LGND AI, Inc. +3
Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection
Mar 27, 2026Gianluca Guglielmo, Marc MasanaDistribution Shift Detection OOD Detection
Graz University of Technology

Apr 28, 2026, cs.CVJ/K move · Enter open · S save

LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images

James Flora, Kowshik Thopalli, Akshay R. Kulkarni, Weng-Keen Wong+1

School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, USA · Lawrence Livermore National Laboratory, Livermore, California, USA · University of California San Diego, California, USA

Distribution Shift Distribution Shift Detection Density Ratio Estimation

Open paper AskPDF arXiv

We present LatentDiff, a scalable framework for semantic dataset comparison that operates directly in the latent space of pretrained vision encoders. By combining sparse autoencoder-based divergence testing with density ratio estimation, LatentDiff identifies interpretable semantic differences between datasets at a fraction of the computational cost of caption-based alternatives. We also introduce Noisy-Diff, a benchmark capturing realistic sparse distribution shifts that cause existing methods to struggle. Experiments demonstrate that LatentDiff achieves superior accuracy while remaining robust to settings where an extremely small fraction of images (from 5% to <1% ) differ semantically.

Momentum

Related topics

Latest papers 36

LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images

Information-Geometric First-Passage Monitoring of Distributional Stability in Stochastic Systems

Component-Based Out-of-Distribution Detection

In-Context Learning Under Regime Change

OT on the Map: Quantifying Domain Shifts in Geographic Space

Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection