Domain Adaptation

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Period ending 2026-09-21

17 new papers

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Period ending 2026-09-14

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Period ending 2026-09-07

18 new papers

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Domain Adaptation

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Distribution Shifts

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Domain Shift

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Cross-Domain Shifts

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Target Domain

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505 papers

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Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification
Jan 23, 2026Tengyue Zhang, Ruiwen Ding, Luoting Zhuang +3Domain Adaptation Medical Image Generation
Factorizable joint shift revisited
Jan 21, 2026Dirk TascheDistribution Shifts Candidate Label Distributions
Uncertainty-Aware Domain Adaptation for Vitiligo Segmentation in Clinical Photographs
Dec 12, 2025Wentao Jiang, Vamsi Varra, Caitlin Perez-Stable +3Skin Lesion Classification Domain Adaptation
Online conformal inference with retrospective adjustment for faster adaptation to distribution shift
Nov 6, 2025Jungbin Jun, Ilsang OhnConformal Inference Conformal Prediction
VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models
Oct 15, 2025Dominick Reilly, Manish Kumar Govind, Le Xue +1Vision-Language Model Adaptation Large Vision Language Models
FedIA: Importance-Aware Aggregation for Domain-Robust Federated Graph Learning
Sep 17, 2025Zhanting Zhou, Zeyu Ma, Ziqiang Zheng +1Federated Graph Learning Fedavg
FedDAF: Federated Domain Adaptation Using Model Functional Distance
Sep 15, 2025Mrinmay Sen, Sidhant Nair, C Krishna MohanFederated Learning Domain Adaptation
WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers
Sep 12, 2025Akshat Pandey, Karun Kumar, Raphael TangDomain Adaptation Variational Autoencoder
Political Ideology Shifts in Large Language Models
Aug 22, 2025Pietro Bernardelle, Stefano Civelli, Leon Fröhling +3Ideology Large Language Model Bias
SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
Jul 24, 2025Ye-Chan Kim, SeungJu Cha, Si-Woo Kim +2Domain Adaptation Synthetic-To-Real Domain Gap
When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts
Jul 19, 2025Wooseok Ha, Yuansi ChenDomain Adaptation Target Domain
A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection
May 19, 2025Sanggeon Yun, Ryozo Masukawa, Hyunwoo Oh +2Adversarial Examples Domain Shift
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Apr 24, 2025Yang Liu, Kejia Zhang, Bingjie Yan +11Domain Adaptation Small Models
AdaptiveCDM: Source-Free Few-Shot Domain Adaptation for Cell Detection in Microscopic Images
Mar 5, 2025Nimra Dilawar, Sara Nadeem, Javed Iqbal +2Domain Adaptation Few-Shot Segmentation
ROAD-Waymo: A Large-Scale Action Awareness Dataset for Autonomous Driving
Nov 3, 2024Salman Khan, Izzeddin Teeti, Reza Javanmard Alitappeh +5Waymo Open Motion Dataset Autonomous Driving
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Graph Representation Learning Distribution Shifts
DIRA-SS:Dynamic Domain Incremental Regularised Adaptation -- Self-Supervised
Nov 13, 2023Abanoub Ghobrial, Kerstin EderDomain Adaptation Self-Supervised
Catalogue Photography as a Cold Start: Toward Deployable Rotary Milling Tool Recognition
Date pendingAbilash Philip Madavath, Chandra Yuvesh Aubeeluck, Augustin Raju +3Computer Vision Domain Shift
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
Date pendingYazhou ZhuFew-Shot Medical Image Segmentation Medical Image Classification
Multi-Source Wasserstein Distributionally Robust Graph Learning
Date pendingChuansen Peng, Yifan Xia, Jinshan Zhong +1Graph Representation Learning Wasserstein Distance
Are We Really Doing Few-Shot Learning? A Critical Examination of Pre-Training Assumptions
Date pendingAlejandro Galan-Cuenca, Marcelo Saval-Calvo, Antonio Javier GallegoFew-Shot Learning Target Domain
R $^2$ M: Real-Aware Residual Model Merging for Robust and Generalizable Deepfake Detection
Date pendingJinhee Park, Guisik Kim, Choongsang Cho +1Explainable Deepfake Detection Deepfake Detection
Guided Adversarial Robust Transfer Learning with Source Mixing
Date pendingXin Xiong, Zijian Guo, Tianxi CaiTransfer Learning Adversarial Transferability
Assessing Predictive Models for Fairness Based on Activity-Space Patterns
Date pendingFrancesco Lettich, Mario A. Nascimento, Chiara Pugliese +1Distribution Shifts Synthetic Dataset
Confidence-Calibrating Regularization for Robust Brain MRI Segmentation Under Domain Shift
Date pendingBehraj Khan, Tahir Qasim Syed, Syed Ahmad Chan BukhariMagnetic Resonance Imaging Segment Anything Model

Jan 23, 2026 · cs.CVJ/K move · Enter open · S save

Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification

Tengyue Zhang, Ruiwen Ding, Luoting Zhuang, Yuxiao Wu+2

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches either fail to leverage unlabeled data from the target domain or rely on image-to-image translation, which can distort tissue structures and compromise model accuracy. In this work, we propose a semi-supervised domain adaptation (SSDA) framework that utilizes a latent diffusion model trained on unlabeled data from both the source and target domains to generate morphology-preserving and target-aware synthetic images. By conditioning the diffusion model on foundation model features, cohort identity, and tissue preparation method, we preserve tissue structure in the source domain while introducing target-domain appearance characteristics. The target-aware synthetic images, combined with real, labeled images from the source cohort, are subsequently used to train a downstream classifier, which is then tested on the target cohort. The effectiveness of the proposed SSDA framework is demonstrated on the task of lung adenocarcinoma prognostication. The proposed augmentation yielded substantially better performance on the held-out test set from the target cohort, without degrading source-cohort performance. The approach improved the weighted F1 score on the target-cohort held-out test set from 0.611 to 0.706 and the macro F1 score from 0.641 to 0.716. Our results demonstrate that target-aware diffusion-based synthetic data augmentation provides a promising and effective approach for improving domain generalization in computational pathology.

Domain Adaptation Medical Image Generation Computational Pathology Synthetic Data Augmentation Latent Diffusion Model Appearance

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