Distribution Matching

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Distribution matching studies how to compare or align probability distributions, often when individual examples cannot be paired, to support generation, transfer, compression, and evaluation. Common tools include optimal transport and Wasserstein distances, kernel maximum mean discrepancy, divergences such as KL and total variation, and moment, quantile, or feature-space matching. These methods are used in generative modeling, dataset distillation, domain adaptation, representation learning, and distribution testing. A central tension is that results depend on the chosen representation and discrepancy: matching global or marginal statistics can miss modes, dependencies, or task-relevant structure, while richer comparisons can be costly or unstable.

Latest papers

80 papers13 papers in the last four weeks, up 225% on the four weeks before. 0.1% of all new papers.

SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
Apr 25, 2026Kaiwen Huang, Yi Zhou, Yizhe Zhang +2Distribution Matching Semi-Supervised Medical Image Segmentation
Nanjing University of Science and Technology, Nanjing, China · Southeast University, Nanjing, China
Simulator Adaptation for Sim-to-Real Learning of Legged Locomotion via Proprioceptive Distribution Matching
Apr 13, 2026Jeremy Dao, Alan FernSim-to-Real Transfer Quadruped Locomotion
Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, Oregon, 97331, USA.
Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation
Apr 3, 2026Jie Yang, Ziqi Ye, Aihua Ke +3Synthetic Data Augmentation Medical Image Synthesis
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China · Shanghai Innovation Institute, Shanghai, China · Shanghai AI Laboratory, Shanghai, China
Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification
Apr 2, 2026Shota Harada, Ryoma Bise, Kiyohito Tanaka +1Medical Image Classification Distribution Matching
Kyushu University, Japan · Kyoto Second Red Cross Hospital, Japan
Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment
Mar 30, 2026Tongfei Liu, Yufan Liu, Bing Li +1Dataset Distillation Dataset Condensation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chines Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, Beijing, China. · School of Artificial Intelligence, University of Chinese Academy of Sciences, 1 Yanqihu East Road, Beijing, 101408, Beijing, China. · PeopleAI, Inc., 68 Zhichun Road, Beijing, 100098, Beijing, China. +1
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
Mar 27, 2026Andrea Fraschini, Davide Tenedini, Riccardo Zamboni +2RL Control Distribution Matching
Politecnico di Milano · Technion
RADIUS: Ranking, Distribution, and Significance - A Comprehensive Alignment Suite for Survey Simulation
Mar 19, 2026Weronika Łajewska, Paul Missault, George Davidson +1Large Language Model-Based Social Simulation Distribution Matching
Amazon Luxembourg, Luxembourg · Amazon Barcelona, Spain
Robust-ComBat: Mitigating Outlier Effects in Diffusion MRI Data Harmonization
Mar 18, 2026Yoan David, Pierre-Marc Jodoin, Alzheimer's Disease Neuroimaging Initiative +1Medical Image Harmonization Medical Image Analysis
VitaLab, Dep of Computer Science, University of Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada · Imeka Solutions Inc., Sherbrooke, J1H 4A7, QC, Canada · A comprehensive list of authors and affiliations of this consortium appear at the end of the paper.
Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning
Mar 11, 2026Zhaowei Zhang, Xiaohan Liu, Xuekai Zhu +6LLM Alignment Distribution Matching
Institute for Artificial Intelligence, Peking University · University of Michigan · Shanghai Jiao Tong University +3
Towards Principled Dataset Distillation: A Spectral Distribution Perspective
Mar 2, 2026Ruixi Wu, Shaobo Wang, Jiahuan Chen +9Dataset Distillation Distribution Matching
EPIC Lab, SJTU · UChicago · NUS +2
CASR: A Robust Cyclic Framework for Arbitrary Large-Scale Super-Resolution with Distribution Alignment and Self-Similarity Awareness
Feb 25, 2026Wenhao Guo, Zhaoran Zhao, Peng Lu +3Image Super-Resolution Distribution Matching
Beijing University of Posts and Telecommunications · Peking University
Convergence Rates for Distribution Matching with Sliced Optimal Transport
Feb 11, 2026Gauthier Thurin, Claire Boyer, Kimia NadjahiDistribution Matching Sliced Wasserstein Distance
ENS-PSL · CNRS, ENS Paris, France · LMO, IUF, CELESTE +2
Deep Time-series Forecasting Needs Kernelized Moment Balancing
Jan 31, 2026Licheng Pan, Haocheng Yang, Shuting He +7Time Series Forecasting Distribution Matching
Xiaohongshu Inc. · National University of Singapore · College of Computer Science and Technology, Zhejiang University
Factorizable joint shift revisited
Jan 21, 2026Dirk TascheDistribution Shift Robustness Label Shift
Centre for Business Mathematics and Informatics, North-West University, South Africa
Revisiting GAN with Bayes-Optimal Discrimination
Oct 29, 2025Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2TV Distance Computation Generative Adversarial Networks
University of Toronto · Stanford University · University of Michigan
Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management
Sep 30, 2025Runze Zhang, Xiaowei Zhang, Mingyang ZhaoLLM Evaluation Human Behavior Simulation
Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
A Discrepancy-Based Perspective on Dataset Condensation
Sep 12, 2025Tong Chen, Raghavendra SelvanDistribution Matching Dataset Distillation
Department of Computer Science, University of Copenhagen, Denmark
Efficient and Stable Multi-Dimensional Kolmogorov-Smirnov Distance
Apr 15, 2025Peter Matthew Jacobs, Foad Namjoo, Jeff M. PhillipsTwo-Sample Testing Distribution Matching
University of Wisconsin–Madison, USA · University of Utah, USA
Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing
Mar 10, 2025Debabrota Basu, Debarshi ChandaTV Distance Computation Wasserstein Distance
Équipe Scool, Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189- CRIStAL F-59000 Lille, France · Indian Statistical Institute Kolkata, India
Bringing Generative Learning to Representation Learning: Self-Supervised Transfer Learning as Distribution Matching
Feb 20, 2025Yuling Jiao, Wensen Ma, Defeng Sun +2Visual Representation Learning Distribution Matching
School of Artificial Intelligence, Wuhan University, 430072, Wuhan, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China +2

Apr 25, 2026J/K move · Enter open · S save

SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation

Kaiwen Huang, Yi Zhou, Yizhe Zhang, Jingxiong Li+1

Nanjing University of Science and Technology, Nanjing, China · Southeast University, Nanjing, China

Distribution Matching Semi-Supervised Medical Image Segmentation Medical Image Segmentation Semi-Supervised Learning Medical Image Representation Learning

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Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabeled data to enhance model performance. Traditional discriminative segmentation relies on segmentation masks, neglecting feature-level distribution constraints. This limits robust semantic representation learning and adaptive modeling of unlabeled data in scenarios with few labels. To address these limitations, we propose SemiGDA, a novel Generative Dual-distribution Alignment framework for semi-supervised medical image segmentation. Our SemiGDA overcomes the reliance of discriminative methods on large labeled datasets by aligning feature and semantic distributions to boost semantic learning and scene adaptability. Specifically, we propose a Dual-distribution Alignment Module (DAM), which employs two structurally distinct encoders to model image and mask feature distributions. It enforces their alignment in the latent space via distributional constraints, establishing structured feature consistency. Moreover, we design a Consistency-Driven Skip Adapter (CDSA) strategy, which introduces dual skip adapters (Image and Mask) to fuse multi-scale features via skip connections. Using a consistency loss, CDSA enhances cross-branch semantic alignment and reinforces fine-grained semantic consistency. Experimental results on diverse medical datasets show that our method outperforms other state-of-the-art semi-supervised segmentation methods. Code is released at: https://github.com/taozh2017/SemiGDA.