{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/unsupervised-domain-adaptation/papers/16","list_of":"/task/unsupervised-domain-adaptation","task":"Unsupervised Domain Adaptation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":16,"pages_in_order":20,"rows_per_page":100,"rows":[1501,1600],"of":1951,"counts":{"archive_papers_tagged":1951,"with_a_code_link":864,"where_syntology_ran_a_sample":200,"not_listed_spam_title":0,"listed":1951,"listed_where_code_ran":200,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":177,"every_run_a_failure_of_syntologys_instrument":23,"listed_with_a_run_with_no_instrument_failure":177,"listed_every_run_a_failure_of_syntologys_instrument":23,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/unsupervised-domain-adaptation","prev":"/task/unsupervised-domain-adaptation/papers/15","next":"/task/unsupervised-domain-adaptation/papers/17","papers":[{"url":null,"slug":"mitigating-uncertainty-of-classifier-for","title":"Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation","date":"2021-07-01","arxiv_id":"2107.00727","repositories_listed":0,"syntology":null},{"url":null,"slug":"polito-iit-submission-to-the-epic-kitchens","title":"PoliTO-IIT Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition","date":"2021-07-01","arxiv_id":"2107.00337","repositories_listed":0,"syntology":null},{"url":null,"slug":"clda-contrastive-learning-for-semi-supervised","title":"CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation","date":"2021-06-30","arxiv_id":"2107.00085","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-domain-adaptation-via-supervised","title":"Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization","date":"2021-06-30","arxiv_id":"2106.16093","repositories_listed":0,"syntology":null},{"url":null,"slug":"autoadapt-automated-segmentation-network","title":"AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation","date":"2021-06-24","arxiv_id":"2106.13227","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-off-the-shelf-source-segmenter-for","title":"Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation","date":"2021-06-23","arxiv_id":"2106.12497","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-self-training-for-cross-domain","title":"Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis","date":"2021-06-23","arxiv_id":"2106.12499","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-separable-disentanglement-for","title":"Enhanced Separable Disentanglement for Unsupervised Domain Adaptation","date":"2021-06-22","arxiv_id":"2106.11915","repositories_listed":0,"syntology":null},{"url":null,"slug":"team-pykale-xy9-submission-to-the-epic","title":"Team PyKale (xy9) Submission to the EPIC-Kitchens 2021 Unsupervised Domain Adaptation Challenge for Action Recognition","date":"2021-06-22","arxiv_id":"2106.12023","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatio-temporal-contrastive-domain-adaptation","title":"Spatio-temporal Contrastive Domain Adaptation for Action Recognition","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transferable-query-selection-for-active","title":"Transferable Query Selection for Active Domain Adaptation","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-reduction-for-model-adaptation-in","title":"Uncertainty Reduction for Model Adaptation in Semantic Segmentation","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"epic-kitchens-100-unsupervised-domain","title":"EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report","date":"2021-06-18","arxiv_id":"2106.10026","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-poisoning-against","title":"On the Effectiveness of Poisoning against Unsupervised Domain Adaptation","date":"2021-06-18","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-dysarthric","title":"Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization","date":"2021-06-18","arxiv_id":"2106.10127","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-consistency-regularization-for","title":"Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification","date":"2021-06-16","arxiv_id":"2106.08590","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-with-2","title":"Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation","date":"2021-06-16","arxiv_id":"2106.08752","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-latent-vector-alignment-for","title":"Optimal Latent Vector Alignment for Unsupervised Domain Adaptation in Medical Image Segmentation","date":"2021-06-15","arxiv_id":"2106.08188","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-domain-adaptation-a-generative-view","title":"Graph Domain Adaptation: A Generative View","date":"2021-06-14","arxiv_id":"2106.07482","repositories_listed":0,"syntology":null},{"url":"/paper/hard-samples-rectification-for-unsupervised","slug":"hard-samples-rectification-for-unsupervised","title":"Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification","date":"2021-06-14","arxiv_id":"2106.07204","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-unsupervised-domain-adaptation-for","title":"Spectral Unsupervised Domain Adaptation for Visual Recognition","date":"2021-06-11","arxiv_id":"2106.06112","repositories_listed":0,"syntology":null},{"url":null,"slug":"afan-augmented-feature-alignment-network-for","title":"AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection","date":"2021-06-10","arxiv_id":"2106.05499","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-predictive-uncertainty-using","title":"Frustratingly Easy Uncertainty Estimation for Distribution Shift","date":"2021-06-07","arxiv_id":"2106.03762","repositories_listed":0,"syntology":null},{"url":"/paper/multi-target-domain-adaptation-with","slug":"multi-target-domain-adaptation-with","title":"Multi-Target Domain Adaptation with Collaborative Consistency Learning","date":"2021-06-07","arxiv_id":"2106.03418","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-domain-adaptation","title":"Generalized Domain Adaptation","date":"2021-06-03","arxiv_id":"2106.01656","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-facial-expression","title":"Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal","date":"2021-06-02","arxiv_id":"2106.01467","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-be-adversarial-let-s-cooperate-1","title":"Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound","date":"2021-06-02","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-models-are-strong","title":"Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners","date":"2021-06-01","arxiv_id":"2106.00417","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-in-cross","title":"Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection","date":"2021-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adaptive-semantic-segmentation","title":"Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint","date":"2021-05-27","arxiv_id":"2105.12939","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaption-of-object","title":"Unsupervised Domain Adaptation of Object Detectors: A Survey","date":"2021-05-27","arxiv_id":"2105.13502","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlated-adversarial-joint-discrepancy","title":"Correlated Adversarial Joint Discrepancy Adaptation Network","date":"2021-05-18","arxiv_id":"2105.08808","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-unsupervised-domain-adaptation-for","title":"Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning","date":"2021-05-17","arxiv_id":"2105.07606","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-super-resolution-of-satellite","title":"Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer","date":"2021-05-16","arxiv_id":"2105.07322","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-via-double","title":"Unsupervised domain adaptation via double classifiers based on high confidence pseudo label","date":"2021-05-11","arxiv_id":"2105.04729","repositories_listed":0,"syntology":null},{"url":null,"slug":"more-separable-and-easier-to-segment-a","title":"More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation","date":"2021-05-07","arxiv_id":"2105.03151","repositories_listed":0,"syntology":null},{"url":"/paper/contrastive-learning-and-self-training-for","slug":"contrastive-learning-and-self-training-for","title":"Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation","date":"2021-05-05","arxiv_id":"2105.02001","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-spherical-manifold-gaussian-kernel-for","title":"Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation","date":"2021-05-05","arxiv_id":"2105.02089","repositories_listed":0,"syntology":null},{"url":null,"slug":"preserving-semantic-consistency-in","title":"Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks","date":"2021-04-28","arxiv_id":"2104.13725","repositories_listed":0,"syntology":null},{"url":null,"slug":"randomized-histogram-matching-a-simple","title":"Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery","date":"2021-04-28","arxiv_id":"2104.14032","repositories_listed":0,"syntology":null},{"url":null,"slug":"strudel-self-training-with-uncertainty","title":"STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains","date":"2021-04-23","arxiv_id":"2104.11596","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-barycenter-flows","title":"Iterative Alignment Flows","date":"2021-04-15","arxiv_id":"2104.07232","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learnable-self-supervised-task-for","title":"A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds","date":"2021-04-12","arxiv_id":"2104.05164","repositories_listed":0,"syntology":null},{"url":null,"slug":"reducing-racial-bias-in-facial-age-prediction","title":"Reducing Racial Bias in Facial Age Prediction using Unsupervised Domain Adaptation in Regression","date":"2021-04-05","arxiv_id":"2104.01781","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-with-global","title":"Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management","date":"2021-04-03","arxiv_id":"2104.01436","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-calibration-for-domain","title":"Confidence Calibration for Domain Generalization under Covariate Shift","date":"2021-04-01","arxiv_id":"2104.00742","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-label-guided-unsupervised-domain","title":"Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-learning-of-neural-transfer-and","title":"Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition","date":"2021-03-31","arxiv_id":"2103.16889","repositories_listed":0,"syntology":null},{"url":"/paper/geometric-unsupervised-domain-adaptation-for","slug":"geometric-unsupervised-domain-adaptation-for","title":"Geometric Unsupervised Domain Adaptation for Semantic Segmentation","date":"2021-03-30","arxiv_id":"2103.16694","repositories_listed":0,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/geometric-unsupervised-domain-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2103.16694","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.16694"}},"official":null}},{"url":null,"slug":"source-free-domain-adaptation-for-semantic","title":"Source-Free Domain Adaptation for Semantic Segmentation","date":"2021-03-30","arxiv_id":"2103.16372","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-pseudo-label-refinement-by-negative","title":"Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation","date":"2021-03-29","arxiv_id":"2103.15973","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-robust-vqa-with-diverse-datasets-and","title":"Domain-robust VQA with diverse datasets and methods but no target labels","date":"2021-03-29","arxiv_id":"2103.15974","repositories_listed":0,"syntology":null},{"url":null,"slug":"get-away-from-style-category-guided-domain","title":"Category-Adaptive Domain Adaptation for Semantic Segmentation","date":"2021-03-29","arxiv_id":"2103.15467","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-playbacks-in-unsupervised-domain-1","title":"Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection","date":"2021-03-26","arxiv_id":"2103.14198","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometry-aware-unsupervised-domain-adaptation","title":"Geometry-Aware Unsupervised Domain Adaptation for Stereo Matching","date":"2021-03-26","arxiv_id":"2103.14333","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-robust-domain-adaptation-without","title":"Unsupervised Robust Domain Adaptation without Source Data","date":"2021-03-26","arxiv_id":"2103.14577","repositories_listed":0,"syntology":null},{"url":null,"slug":"vdm-da-virtual-domain-modeling-for-source","title":"VDM-DA: Virtual Domain Modeling for Source Data-free Domain Adaptation","date":"2021-03-26","arxiv_id":"2103.14357","repositories_listed":0,"syntology":null},{"url":null,"slug":"inferring-latent-domains-for-unsupervised","title":"Inferring Latent Domains for Unsupervised Deep Domain Adaptation","date":"2021-03-25","arxiv_id":"2103.13873","repositories_listed":0,"syntology":null},{"url":"/paper/on-evolving-attention-towards-domain","slug":"on-evolving-attention-towards-domain","title":"On Evolving Attention Towards Domain Adaptation","date":"2021-03-25","arxiv_id":"2103.13561","repositories_listed":0,"syntology":null},{"url":"/paper/coarse-to-fine-domain-adaptive-semantic","slug":"coarse-to-fine-domain-adaptive-semantic","title":"Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization","date":"2021-03-24","arxiv_id":"2103.13041","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlan-multi-level-adversarial-network-for","title":"MLAN: Multi-Level Adversarial Network for Domain Adaptive Semantic Segmentation","date":"2021-03-24","arxiv_id":"2103.12991","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-via-coarse-to","title":"Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning","date":"2021-03-23","arxiv_id":"2103.12371","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-weighted-learning-for-unsupervised","title":"Dynamic Weighted Learning for Unsupervised Domain Adaptation","date":"2021-03-22","arxiv_id":"2103.13814","repositories_listed":0,"syntology":null},{"url":null,"slug":"re-energizing-domain-discriminator-with","title":"Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation","date":"2021-03-22","arxiv_id":"2103.11661","repositories_listed":0,"syntology":null},{"url":null,"slug":"conda-continual-unsupervised-domain","title":"ConDA: Continual Unsupervised Domain Adaptation","date":"2021-03-19","arxiv_id":"2103.11056","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-domain-agnostic-visual","title":"Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss","date":"2021-03-10","arxiv_id":"2103.05902","repositories_listed":0,"syntology":null},{"url":"/paper/multi-source-domain-adaptation-with","slug":"multi-source-domain-adaptation-with","title":"Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation","date":"2021-03-08","arxiv_id":"2103.04717","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptive-robotic-gesture-recognition","title":"Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment","date":"2021-03-06","arxiv_id":"2103.04075","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-image","title":"Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning","date":"2021-03-04","arxiv_id":"2103.02808","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-network-with","title":"Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation","date":"2021-03-03","arxiv_id":"2103.02220","repositories_listed":0,"syntology":null},{"url":null,"slug":"pseudo-labeling-for-scalable-3d-object","title":"Pseudo-labeling for Scalable 3D Object Detection","date":"2021-03-02","arxiv_id":"2103.02093","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulation-to-real-domain-adaptation-with","title":"Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation","date":"2021-03-02","arxiv_id":"2103.01593","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-cross","title":"Unsupervised Domain Adaptation for Cross-Subject Few-Shot Neurological Symptom Detection","date":"2021-02-28","arxiv_id":"2103.00606","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explaining-expressive-qualities-in","title":"Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation","date":"2021-02-26","arxiv_id":"2102.13479","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximizing-cosine-similarity-between-spatial","title":"Maximizing Cosine Similarity Between Spatial Features for Unsupervised Domain Adaptation in Semantic Segmentation","date":"2021-02-25","arxiv_id":"2102.13002","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-noisy-label-learning-for","title":"Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation","date":"2021-02-23","arxiv_id":"2102.11614","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-deep-features-for-shape","title":"Learning Deep Features for Shape Correspondence with Domain Invariance","date":"2021-02-21","arxiv_id":"2102.10493","repositories_listed":0,"syntology":null},{"url":null,"slug":"generation-for-adaption-a-gan-based-approach","title":"Generation For Adaption: A GAN-Based Approach for 3D Domain Adaption with Point Cloud Data","date":"2021-02-15","arxiv_id":"2102.07373","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-unsupervised-domain-adaptation-1","title":"Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection","date":"2021-02-13","arxiv_id":"2102.06864","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-source-pseudo-label-learning-of","title":"Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots","date":"2021-02-12","arxiv_id":"2102.06386","repositories_listed":0,"syntology":null},{"url":null,"slug":"selecting-treatment-effects-models-for-domain-1","title":"Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge","date":"2021-02-11","arxiv_id":"2102.06271","repositories_listed":0,"syntology":null},{"url":null,"slug":"complementary-pseudo-labels-for-unsupervised","title":"Complementary Pseudo Labels For Unsupervised Domain Adaptation On Person Re-identification","date":"2021-01-29","arxiv_id":"2101.12521","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-symmetric-adaptation-network-for-cross","title":"Deep Symmetric Adaptation Network for Cross-modality Medical Image Segmentation","date":"2021-01-18","arxiv_id":"2101.06853","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-distillation-methods-for-efficient","title":"Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains","date":"2021-01-18","arxiv_id":"2101.07308","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-impressions-mining-deep-models-to","title":"Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data","date":"2021-01-15","arxiv_id":"2101.06069","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-by-guessing-multi-step-pseudo-label","title":"Learn by Guessing: Multi-Step Pseudo-Label Refinement for Person Re-Identification","date":"2021-01-04","arxiv_id":"2101.01215","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-unsupervised-domain-adaptation","title":"Adversarial Unsupervised Domain Adaptation for Harmonic-Percussive Source Separation","date":"2021-01-03","arxiv_id":"2101.00701","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-unified-information-regularization","title":"A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"active-universal-domain-adaptation","title":"Active Universal Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-adversarial-network-for-source-free","title":"Adaptive Adversarial Network for Source-Free Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-tree-wasserstein-minimization-for","title":"Adaptive Tree Wasserstein Minimization for Hierarchical Generative Modeling","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-normalization-for-unsupervised","title":"Collaborative Normalization for Unsupervised Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-optimization-and-aggregation","title":"Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disentangled-cyclic-reconstruction-for-domain","title":"Disentangled cyclic reconstruction for domain adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-adversarial-training-for-unsupervised","title":"Dual Adversarial Training for Unsupervised Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emtl-a-generative-domain-adaptation-approach","title":"EMTL: A Generative Domain Adaptation Approach","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-constrained-self-training-for","title":"Energy-constrained Self-training for Unsupervised Domain Adaptation","date":"2021-01-01","arxiv_id":"2101.00316","repositories_listed":0,"syntology":null},{"url":null,"slug":"f-domain-adversarial-learning-theory-and","title":"f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neighbor-class-consistency-on-unsupervised","title":"Neighbor Class Consistency on Unsupervised Domain Adaptation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-unsupervised-domain-adaptation","title":"Scaling Unsupervised Domain Adaptation through Optimal Collaborator Selection and Lazy Discriminator Synchronization","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"1b0963093415c6775dcd0104223abf61e7c7179c5b97986576d3206503ae3234","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}