{"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/self-supervised-learning/papers/25","list_of":"/task/self-supervised-learning","task":"Self-Supervised Learning","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":25,"pages_in_order":51,"rows_per_page":100,"rows":[2401,2500],"of":5044,"counts":{"archive_papers_tagged":5044,"with_a_code_link":2293,"where_syntology_ran_a_sample":666,"not_listed_spam_title":0,"listed":5044,"listed_where_code_ran":666,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":581,"every_run_a_failure_of_syntologys_instrument":85,"listed_with_a_run_with_no_instrument_failure":581,"listed_every_run_a_failure_of_syntologys_instrument":85,"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/self-supervised-learning","prev":"/task/self-supervised-learning/papers/24","next":"/task/self-supervised-learning/papers/26","papers":[{"url":null,"slug":"multimodal-deep-learning-for-stroke","title":"Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data","date":"2025-05-05","arxiv_id":"2505.02677","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-user-sequence-modeling-through","title":"Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning","date":"2025-05-02","arxiv_id":"2505.00953","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-monocular-visual-drone-model","title":"Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling","date":"2025-04-30","arxiv_id":"2504.21695","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-internal-representation-of-self","title":"Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects","date":"2025-04-29","arxiv_id":"2504.20364","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextures-the-mechanism-of-representation","title":"Contextures: The Mechanism of Representation Learning","date":"2025-04-28","arxiv_id":"2504.19792","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-pretraining-for-material-property","title":"Supervised Pretraining for Material Property Prediction","date":"2025-04-27","arxiv_id":"2504.20112","repositories_listed":0,"syntology":null},{"url":null,"slug":"speaker-diarization-for-low-resource","title":"Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning","date":"2025-04-23","arxiv_id":"2504.18582","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-time-series-signal-analysis-with","title":"Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications","date":"2025-04-23","arxiv_id":"2504.16972","repositories_listed":0,"syntology":null},{"url":null,"slug":"full-waveform-inversion-with-cnn-based","title":"Full waveform inversion with CNN-based velocity representation extension","date":"2025-04-22","arxiv_id":"2504.15826","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-learning-method-for-raman","title":"A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders","date":"2025-04-21","arxiv_id":"2504.16130","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-measurement-of-eczema-severity-with","title":"Automated Measurement of Eczema Severity with Self-Supervised Learning","date":"2025-04-21","arxiv_id":"2504.15193","repositories_listed":0,"syntology":null},{"url":null,"slug":"landmark-free-preoperative-to-intraoperative","title":"Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection","date":"2025-04-21","arxiv_id":"2504.15152","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-primal-dual-splitting-for-self","title":"Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction","date":"2025-04-21","arxiv_id":"2504.15390","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-feature-representations-for-marmoset-vocal","title":"On feature representations for marmoset vocal communication analysis","date":"2025-04-21","arxiv_id":"2504.14981","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-we-ignore-labels-in-out-of-distribution","title":"Can We Ignore Labels In Out of Distribution Detection?","date":"2025-04-20","arxiv_id":"2504.14704","repositories_listed":0,"syntology":null},{"url":null,"slug":"hfbri-mae-handcrafted-feature-based-rotation","title":"HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis","date":"2025-04-19","arxiv_id":"2504.14132","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirically-grounded-identifiability","title":"An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research","date":"2025-04-17","arxiv_id":"2504.13101","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-masked-autoencoders-also-listen-to-birds","title":"Can Masked Autoencoders Also Listen to Birds?","date":"2025-04-17","arxiv_id":"2504.12880","repositories_listed":0,"syntology":null},{"url":null,"slug":"psg-mae-robust-multitask-sleep-event","title":"PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning","date":"2025-04-17","arxiv_id":"2504.13229","repositories_listed":0,"syntology":null},{"url":null,"slug":"sar-object-detection-with-self-supervised","title":"SAR Object Detection with Self-Supervised Pretraining and Curriculum-Aware Sampling","date":"2025-04-17","arxiv_id":"2504.13310","repositories_listed":0,"syntology":null},{"url":null,"slug":"h-3-gnns-harmonizing-heterophily-and","title":"H$^3$GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning","date":"2025-04-16","arxiv_id":"2504.11699","repositories_listed":0,"syntology":null},{"url":null,"slug":"radler-radar-object-detection-leveraging","title":"RADLER: Radar Object Detection Leveraging Semantic 3D City Models and Self-Supervised Radar-Image Learning","date":"2025-04-16","arxiv_id":"2504.12167","repositories_listed":0,"syntology":null},{"url":null,"slug":"securing-the-skies-a-comprehensive-survey-on","title":"Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions","date":"2025-04-16","arxiv_id":"2504.11967","repositories_listed":0,"syntology":null},{"url":null,"slug":"sidme-self-supervised-image-demoireing-via","title":"SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction","date":"2025-04-16","arxiv_id":"2504.12245","repositories_listed":0,"syntology":null},{"url":null,"slug":"prototype-guided-diffusion-for-digital","title":"Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data","date":"2025-04-15","arxiv_id":"2504.12351","repositories_listed":0,"syntology":null},{"url":null,"slug":"respiratory-inhaler-sound-event","title":"Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning","date":"2025-04-15","arxiv_id":"2504.11246","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-universal-graph-structural-encoder","title":"Towards A Universal Graph Structural Encoder","date":"2025-04-15","arxiv_id":"2504.10917","repositories_listed":0,"syntology":null},{"url":null,"slug":"infomae-pair-efficient-cross-modal-alignment","title":"InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals","date":"2025-04-13","arxiv_id":"2504.09707","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-large-self-supervised-time-series","title":"Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System","date":"2025-04-12","arxiv_id":"2504.09090","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodied-image-captioning-self-supervised","title":"Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions","date":"2025-04-11","arxiv_id":"2504.08531","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-language-guidance-a-reproducibility","title":"Impact of Language Guidance: A Reproducibility Study","date":"2025-04-10","arxiv_id":"2504.08140","repositories_listed":0,"syntology":null},{"url":null,"slug":"jepa4rec-learning-effective-language","title":"JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture","date":"2025-04-10","arxiv_id":"2504.10512","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-object-focused-attention","title":"Learning Object Focused Attention","date":"2025-04-10","arxiv_id":"2504.08166","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-efficacy-of-semantics-preserving","title":"The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound","date":"2025-04-10","arxiv_id":"2504.07904","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-deep-learning-approach-for-non-invasive","title":"Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI","date":"2025-04-09","arxiv_id":"2504.07246","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolutionary-algorithms-meet-self-supervised","title":"Evolutionary algorithms meet self-supervised learning: a comprehensive survey","date":"2025-04-09","arxiv_id":"2504.07213","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-framework-for-space-object","title":"A Self-Supervised Framework for Space Object Behaviour Characterisation","date":"2025-04-08","arxiv_id":"2504.06176","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-network-based-distributed","title":"Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach","date":"2025-04-08","arxiv_id":"2504.06439","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-auto-distillation-and-generative","title":"Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems","date":"2025-04-08","arxiv_id":"2504.10500","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-the-gap-between-continuous-and","title":"Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization","date":"2025-04-07","arxiv_id":"2504.04721","repositories_listed":0,"syntology":null},{"url":null,"slug":"uni4d-a-unified-self-supervised-learning","title":"Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos","date":"2025-04-07","arxiv_id":"2504.04837","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-self-supervised-learning","title":"Variational Self-Supervised Learning","date":"2025-04-06","arxiv_id":"2504.04318","repositories_listed":0,"syntology":null},{"url":null,"slug":"lv-mae-learning-long-video-representations","title":"LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders","date":"2025-04-04","arxiv_id":"2504.03501","repositories_listed":0,"syntology":null},{"url":null,"slug":"mimrs-a-survey-on-masked-image-modeling-in","title":"MIMRS: A Survey on Masked Image Modeling in Remote Sensing","date":"2025-04-04","arxiv_id":"2504.03181","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-encoder-nnu-net-outperforms-transformer","title":"Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining","date":"2025-04-04","arxiv_id":"2504.03474","repositories_listed":0,"syntology":null},{"url":null,"slug":"qirl-boosting-visual-question-answering-via","title":"QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning","date":"2025-04-04","arxiv_id":"2504.03337","repositories_listed":0,"syntology":null},{"url":null,"slug":"ringmoe-mixture-of-modality-experts-multi","title":"RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation","date":"2025-04-04","arxiv_id":"2504.03166","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-computation-and-communication","title":"Towards Computation- and Communication-efficient Computational Pathology","date":"2025-04-03","arxiv_id":"2504.02628","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-plasticity-aware-method-for-continual-self","title":"A Plasticity-Aware Method for Continual Self-Supervised Learning in Remote Sensing","date":"2025-03-31","arxiv_id":"2503.24088","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-velocity-and-acceleration-self","title":"Learning Velocity and Acceleration: Self-Supervised Motion Consistency for Pedestrian Trajectory Prediction","date":"2025-03-31","arxiv_id":"2503.24272","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathorchestra-a-comprehensive-foundation","title":"PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks","date":"2025-03-31","arxiv_id":"2503.24345","repositories_listed":0,"syntology":null},{"url":null,"slug":"au-ttt-vision-test-time-training-model-for","title":"AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection","date":"2025-03-30","arxiv_id":"2503.23450","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-self-supervised-learning-for-one","title":"Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation","date":"2025-03-30","arxiv_id":"2503.23507","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-self-supervised-learning-of-a-foundation","title":"A Self-Supervised Learning of a Foundation Model for Analog Layout Design Automation","date":"2025-03-28","arxiv_id":"2503.22143","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-building-roof-type-classification-a","title":"Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach","date":"2025-03-28","arxiv_id":"2503.22251","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-library-cell-representations-in","title":"Learning Library Cell Representations in Vector Space","date":"2025-03-28","arxiv_id":"2503.22900","repositories_listed":0,"syntology":null},{"url":null,"slug":"masked-self-supervised-pre-training-for-text","title":"Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets","date":"2025-03-28","arxiv_id":"2503.22513","repositories_listed":0,"syntology":null},{"url":null,"slug":"magnitude-phase-dual-path-speech-enhancement","title":"Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch Boosting","date":"2025-03-27","arxiv_id":"2503.21571","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-sequence-augmenter-for-triplet","title":"Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation","date":"2025-03-26","arxiv_id":"2503.20232","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-object-detection-a-comprehensive-survey","title":"Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications","date":"2025-03-26","arxiv_id":"2503.20516","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-low-rank-structures-in-the","title":"An Overview of Low-Rank Structures in the Training and Adaptation of Large Models","date":"2025-03-25","arxiv_id":"2503.19859","repositories_listed":0,"syntology":null},{"url":null,"slug":"d2sa-dual-stage-distribution-and-slice","title":"D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction","date":"2025-03-25","arxiv_id":"2503.20815","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-motion-concepts","title":"Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals","date":"2025-03-25","arxiv_id":"2503.19953","repositories_listed":0,"syntology":null},{"url":null,"slug":"social-network-user-profiling-for-anomaly","title":"Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks","date":"2025-03-25","arxiv_id":"2503.19380","repositories_listed":0,"syntology":null},{"url":null,"slug":"foundation-model-for-whole-heart-segmentation","title":"Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging","date":"2025-03-24","arxiv_id":"2503.19005","repositories_listed":0,"syntology":null},{"url":null,"slug":"hires-fusedmim-a-high-resolution-rgb-dsm-pre","title":"HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications","date":"2025-03-24","arxiv_id":"2503.18540","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-based-on-transformed","title":"Self-Supervised Learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation","date":"2025-03-24","arxiv_id":"2503.18753","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-xl-pro-reconstructive-token-compression","title":"Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding","date":"2025-03-24","arxiv_id":"2503.18478","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-feature-interaction-via","title":"Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data","date":"2025-03-23","arxiv_id":"2503.18048","repositories_listed":0,"syntology":null},{"url":null,"slug":"pim-physics-informed-multi-task-pre-training","title":"PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition","date":"2025-03-23","arxiv_id":"2503.17978","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-representational-predictive-coding","title":"Meta-Representational Predictive Coding: Biomimetic Self-Supervised Learning","date":"2025-03-22","arxiv_id":"2503.21796","repositories_listed":0,"syntology":null},{"url":null,"slug":"modaltune-fine-tuning-slide-level-foundation","title":"ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology","date":"2025-03-21","arxiv_id":"2503.17564","repositories_listed":0,"syntology":null},{"url":null,"slug":"should-we-pre-train-a-decoder-in-contrastive","title":"Should we pre-train a decoder in contrastive learning for dense prediction tasks?","date":"2025-03-21","arxiv_id":"2503.17526","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-efficiently-adapt-foundation","title":"Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras","date":"2025-03-20","arxiv_id":"2503.15917","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-noise-masked-modeling-for-video","title":"Structured-Noise Masked Modeling for Video, Audio and Beyond","date":"2025-03-20","arxiv_id":"2503.16311","repositories_listed":0,"syntology":null},{"url":null,"slug":"1000-layer-networks-for-self-supervised-rl","title":"1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities","date":"2025-03-19","arxiv_id":"2503.14858","repositories_listed":0,"syntology":null},{"url":null,"slug":"climategs-real-time-climate-simulation-with","title":"ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer","date":"2025-03-19","arxiv_id":"2503.14845","repositories_listed":0,"syntology":null},{"url":null,"slug":"conjuring-positive-pairs-for-efficient","title":"Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis","date":"2025-03-19","arxiv_id":"2503.15060","repositories_listed":0,"syntology":null},{"url":null,"slug":"semanticflow-a-self-supervised-framework-for","title":"SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments","date":"2025-03-19","arxiv_id":"2503.14837","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-attributes-and-multi-scale","title":"Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning","date":"2025-03-18","arxiv_id":"2503.13911","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtloc-a-confidence-based-source-free-domain","title":"MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization","date":"2025-03-18","arxiv_id":"2503.14767","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-self-supervised-semantic","title":"Multi-Modal Self-Supervised Semantic Communication","date":"2025-03-18","arxiv_id":"2503.13940","repositories_listed":0,"syntology":null},{"url":"/paper/psa-ssl-pose-and-size-aware-self-supervised","slug":"psa-ssl-pose-and-size-aware-self-supervised","title":"PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds","date":"2025-03-18","arxiv_id":"2503.13914","repositories_listed":0,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":4,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/psa-ssl-pose-and-size-aware-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2503.13914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.13914"}},"official":null}},{"url":null,"slug":"romedformer-a-rotary-embedding-transformer","title":"RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT","date":"2025-03-18","arxiv_id":"2503.14304","repositories_listed":0,"syntology":null},{"url":null,"slug":"text-guided-image-invariant-feature-learning","title":"Text-Guided Image Invariant Feature Learning for Robust Image Watermarking","date":"2025-03-18","arxiv_id":"2503.13805","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-scalable-foundation-model-for-multi","title":"Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data","date":"2025-03-17","arxiv_id":"2503.12843","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-self-supervised-contrastive","title":"A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis","date":"2025-03-14","arxiv_id":"2503.11101","repositories_listed":0,"syntology":null},{"url":null,"slug":"bioserenity-e1-a-self-supervised-eeg-model","title":"BioSerenity-E1: a self-supervised EEG model for medical applications","date":"2025-03-13","arxiv_id":"2503.10362","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-visual-explanations-of-attention","title":"Evaluating Visual Explanations of Attention Maps for Transformer-based Medical Imaging","date":"2025-03-12","arxiv_id":"2503.09535","repositories_listed":0,"syntology":null},{"url":null,"slug":"open-world-skill-discovery-from-unsegmented","title":"Open-World Skill Discovery from Unsegmented Demonstrations","date":"2025-03-11","arxiv_id":"2503.10684","repositories_listed":0,"syntology":null},{"url":"/paper/can-generative-geospatial-diffusion-models","slug":"can-generative-geospatial-diffusion-models","title":"Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?","date":"2025-03-10","arxiv_id":"2503.07890","repositories_listed":0,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/can-generative-geospatial-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2503.07890","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.07890"}},"official":null}},{"url":null,"slug":"divide-and-conquer-self-supervised-learning","title":"Divide and Conquer Self-Supervised Learning for High-Content Imaging","date":"2025-03-10","arxiv_id":"2503.07444","repositories_listed":0,"syntology":null},{"url":null,"slug":"endo-fast3r-endoscopic-foundation-model","title":"Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion","date":"2025-03-10","arxiv_id":"2503.07204","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-unsupervised-annotation-of-c-elegans","title":"Fully Unsupervised Annotation of C. Elegans","date":"2025-03-10","arxiv_id":"2503.07348","repositories_listed":0,"syntology":null},{"url":null,"slug":"miram-masked-image-reconstruction-across","title":"MIRAM: Masked Image Reconstruction Across Multiple Scales for Breast Lesion Risk Prediction","date":"2025-03-10","arxiv_id":"2503.07157","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-overlapping-prediction-a-self","title":"Temporal Overlapping Prediction: A Self-supervised Pre-training Method for LiDAR Moving Object Segmentation","date":"2025-03-10","arxiv_id":"2503.07167","repositories_listed":0,"syntology":null},{"url":null,"slug":"pathvq-reforming-computational-pathology","title":"PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantization","date":"2025-03-09","arxiv_id":"2503.06482","repositories_listed":0,"syntology":null},{"url":null,"slug":"ti-jepa-an-innovative-energy-based-joint","title":"TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems","date":"2025-03-09","arxiv_id":"2503.06380","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-robustness-of-discriminative-self","title":"Adversarial Robustness of Discriminative Self-Supervised Learning in Vision","date":"2025-03-08","arxiv_id":"2503.06361","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-models-for-phoneme","title":"Self-Supervised Models for Phoneme Recognition: Applications in Children's Speech for Reading Learning","date":"2025-03-06","arxiv_id":"2503.04710","repositories_listed":0,"syntology":null}],"record_sha256":"bd31481833557c495ff345f77902aa76b746b521d6985987c538d4f907f8e9eb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}