{"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/data-augmentation/papers/43","list_of":"/task/data-augmentation","task":"Data Augmentation","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":43,"pages_in_order":84,"rows_per_page":100,"rows":[4201,4300],"of":8378,"counts":{"archive_papers_tagged":8378,"with_a_code_link":3225,"where_syntology_ran_a_sample":692,"not_listed_spam_title":0,"listed":8378,"listed_where_code_ran":692,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":567,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":567,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/data-augmentation","prev":"/task/data-augmentation/papers/42","next":"/task/data-augmentation/papers/44","papers":[{"url":null,"slug":"fiesta-fourier-based-semantic-augmentation","title":"FIESTA: Fourier-Based Semantic Augmentation with Uncertainty Guidance for Enhanced Domain Generalizability in Medical Image Segmentation","date":"2024-06-20","arxiv_id":"2406.14308","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-pretext-tasks-for-alzheimer-s","title":"Self-Supervised Pretext Tasks for Alzheimer's Disease Classification using 3D Convolutional Neural Networks on Large-Scale Synthetic Neuroimaging Dataset","date":"2024-06-20","arxiv_id":"2406.14210","repositories_listed":0,"syntology":null},{"url":null,"slug":"urban-focused-multi-task-offline","title":"Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing","date":"2024-06-20","arxiv_id":"2406.14054","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-zero-shot-cross-lingual-transfer-3","title":"Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching","date":"2024-06-19","arxiv_id":"2406.13361","repositories_listed":0,"syntology":null},{"url":null,"slug":"agriculture-vision-challenge-2024-the-runner","title":"Agriculture-Vision Challenge 2024 -- The Runner-Up Solution for Agricultural Pattern Recognition via Class Balancing and Model Ensemble","date":"2024-06-18","arxiv_id":"2406.12271","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-visual-appearances-privacy-sensitive","title":"Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning","date":"2024-06-18","arxiv_id":"2406.12736","repositories_listed":0,"syntology":null},{"url":null,"slug":"composited-nested-learning-with-data","title":"Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition","date":"2024-06-18","arxiv_id":"2406.12779","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-anywhere-enhancing-360-monocular-depth","title":"Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation","date":"2024-06-18","arxiv_id":"2406.12849","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-your-hd-map-constructor-reliable-under","title":"Is Your HD Map Constructor Reliable under Sensor Corruptions?","date":"2024-06-18","arxiv_id":"2406.12214","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmutf-multimodal-multimedia-event-argument","title":"MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling","date":"2024-06-18","arxiv_id":"2406.12420","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-time-series-anomaly-detection","title":"Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation","date":"2024-06-18","arxiv_id":"2406.12260","repositories_listed":0,"syntology":null},{"url":null,"slug":"skin-cancer-images-classification-using","title":"Skin Cancer Images Classification using Transfer Learning Techniques","date":"2024-06-18","arxiv_id":"2406.12954","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminative-hamiltonian-variational","title":"Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes","date":"2024-06-17","arxiv_id":"2406.11659","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-generalization-for-in-orbit-6d-pose","title":"Domain Generalization for In-Orbit 6D Pose Estimation","date":"2024-06-17","arxiv_id":"2406.11743","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-and-testing-instruction-following","title":"Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants","date":"2024-06-17","arxiv_id":"2406.11301","repositories_listed":0,"syntology":null},{"url":null,"slug":"p-ta-using-proximal-policy-optimization-to","title":"P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models","date":"2024-06-17","arxiv_id":"2406.11391","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-large-language-models-for-2","title":"A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges","date":"2024-06-15","arxiv_id":"2406.11903","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-latent-perspective-learning-for","title":"Discrete Latent Perspective Learning for Segmentation and Detection","date":"2024-06-15","arxiv_id":"2406.10475","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-data-augmentation-algorithm","title":"The data augmentation algorithm","date":"2024-06-15","arxiv_id":"2406.10464","repositories_listed":0,"syntology":null},{"url":null,"slug":"inclusive-asr-for-disfluent-speech-cascaded","title":"Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation","date":"2024-06-14","arxiv_id":"2406.10177","repositories_listed":0,"syntology":null},{"url":null,"slug":"roar-reinforcing-original-to-augmented-data","title":"ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR","date":"2024-06-14","arxiv_id":"2406.09999","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-free-camera-control-for-video","title":"Training-free Camera Control for Video Generation","date":"2024-06-14","arxiv_id":"2406.10126","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-psychotherapy-counseling-a-data","title":"Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations","date":"2024-06-13","arxiv_id":"2406.08718","repositories_listed":0,"syntology":null},{"url":null,"slug":"plan-generate-and-complicate-improving-low","title":"Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation","date":"2024-06-13","arxiv_id":"2406.08860","repositories_listed":0,"syntology":null},{"url":null,"slug":"simgen-simulator-conditioned-driving-scene","title":"SimGen: Simulator-conditioned Driving Scene Generation","date":"2024-06-13","arxiv_id":"2406.09386","repositories_listed":0,"syntology":null},{"url":null,"slug":"svitt-ego-a-sparse-video-text-transformer-for","title":"SViTT-Ego: A Sparse Video-Text Transformer for Egocentric Video","date":"2024-06-13","arxiv_id":"2406.09462","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-jepa-a-joint-embedding-predictive","title":"T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation","date":"2024-06-13","arxiv_id":"2406.12913","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-mode-decomposition-as-trusted","title":"Variational Mode Decomposition as Trusted Data Augmentation in ML-based Power System Stability Assessment","date":"2024-06-13","arxiv_id":"2406.09235","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-don-t-need-data-augmentation-in-self","title":"You Don't Need Domain-Specific Data Augmentations When Scaling Self-Supervised Learning","date":"2024-06-13","arxiv_id":"2406.09294","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffpop-plausibility-guided-object-placement","title":"DiffPop: Plausibility-Guided Object Placement Diffusion for Image Composition","date":"2024-06-12","arxiv_id":"2406.07852","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-fuzzing-as-data-augmentation-for","title":"Data Augmentation by Fuzzing for Neural Test Generation","date":"2024-06-12","arxiv_id":"2406.08665","repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-realistic-post-stroke-reaching","title":"Enhancing Activity Recognition After Stroke: Generative Adversarial Networks for Kinematic Data Augmentation","date":"2024-06-12","arxiv_id":"2406.09451","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-context-prompting-boosts-fairness-and","title":"Test-Time Fairness and Robustness in Large Language Models","date":"2024-06-11","arxiv_id":"2406.07685","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-the-impact-of-noisy-labels-in","title":"Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective","date":"2024-06-11","arxiv_id":"2406.07314","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-data-augmentation-methods-for-end","title":"Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems","date":"2024-06-10","arxiv_id":"2406.06127","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-multivariate-time","title":"Data Augmentation for Multivariate Time Series Classification: An Experimental Study","date":"2024-06-10","arxiv_id":"2406.06518","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-in-earth-observation-a","title":"Data Augmentation in Earth Observation: A Diffusion Model Approach","date":"2024-06-10","arxiv_id":"2406.06218","repositories_listed":0,"syntology":null},{"url":null,"slug":"equivariant-neural-tangent-kernels","title":"Equivariant Neural Tangent Kernels","date":"2024-06-10","arxiv_id":"2406.06504","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-deep-learning-based-automatic","title":"Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models","date":"2024-06-10","arxiv_id":"2406.06372","repositories_listed":0,"syntology":null},{"url":null,"slug":"solution-for-cvpr-2024-ug2-challenge-track-on","title":"Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation","date":"2024-06-09","arxiv_id":"2406.05837","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-time-series-to-image-encoding","title":"A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification","date":"2024-06-07","arxiv_id":"2406.05096","repositories_listed":0,"syntology":null},{"url":null,"slug":"clarifying-myths-about-the-relationship","title":"Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness","date":"2024-06-07","arxiv_id":"2406.05006","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-indoor-temperature-forecasting","title":"Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments","date":"2024-06-07","arxiv_id":"2406.04890","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-effectiveness-of-data","title":"Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings","date":"2024-06-07","arxiv_id":"2406.05190","repositories_listed":0,"syntology":null},{"url":null,"slug":"atradiff-accelerating-online-reinforcement","title":"ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories","date":"2024-06-06","arxiv_id":"2406.04323","repositories_listed":0,"syntology":null},{"url":null,"slug":"cut-and-paste-with-precision-a-content-and","title":"Cut-and-Paste with Precision: a Content and Perspective-aware Data Augmentation for Road Damage Detection","date":"2024-06-06","arxiv_id":"2406.18586","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-traffic-sign-recognition-with","title":"Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity","date":"2024-06-05","arxiv_id":"2406.03576","repositories_listed":0,"syntology":null},{"url":null,"slug":"get-a-generative-eeg-transformer-for","title":"GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals","date":"2024-06-05","arxiv_id":"2406.03115","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-guided-detection-and-mitigation-of","title":"Language-guided Detection and Mitigation of Unknown Dataset Bias","date":"2024-06-05","arxiv_id":"2406.02889","repositories_listed":0,"syntology":null},{"url":null,"slug":"readability-guided-idiom-aware-sentence","title":"Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese","date":"2024-06-05","arxiv_id":"2406.02974","repositories_listed":0,"syntology":null},{"url":null,"slug":"inpainting-pathology-in-lumbar-spine-mri-with","title":"Inpainting Pathology in Lumbar Spine MRI with Latent Diffusion","date":"2024-06-04","arxiv_id":"2406.02477","repositories_listed":0,"syntology":null},{"url":null,"slug":"translation-deserves-better-analyzing","title":"Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering","date":"2024-06-04","arxiv_id":"2406.02331","repositories_listed":0,"syntology":null},{"url":null,"slug":"ed-sam-an-efficient-diffusion-sampling","title":"ED-SAM: An Efficient Diffusion Sampling Approach to Domain Generalization in Vision-Language Foundation Models","date":"2024-06-03","arxiv_id":"2406.01432","repositories_listed":0,"syntology":null},{"url":null,"slug":"emoe-expansive-matching-of-experts-for-robust","title":"EMOE: Expansive Matching of Experts for Robust Uncertainty Based Rejection","date":"2024-06-03","arxiv_id":"2406.01825","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixup-augmentation-with-multiple","title":"Mixup Augmentation with Multiple Interpolations","date":"2024-06-03","arxiv_id":"2406.01417","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-informed-augmentation-for-robust","title":"Sensitivity-Informed Augmentation for Robust Segmentation","date":"2024-06-03","arxiv_id":"2406.01425","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmentation-based-unsupervised-cross-domain","title":"Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification","date":"2024-05-31","arxiv_id":"2406.00085","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-based-time-series-data-augmentation-to","title":"Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction","date":"2024-05-31","arxiv_id":"2405.20590","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-generalization-for-retinal-vessel","title":"Domain generalization for retinal vessel segmentation via Hessian-based vector field","date":"2024-05-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-class-fairness-in-classification","title":"Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach","date":"2024-05-31","arxiv_id":"2407.03146","repositories_listed":0,"syntology":null},{"url":null,"slug":"genmix-combining-generative-and-mixture-data","title":"GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification","date":"2024-05-31","arxiv_id":"2405.20650","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvad-a-multiple-visual-artifact-detector-for","title":"MVAD: A Multiple Visual Artifact Detector for Video Streaming","date":"2024-05-31","arxiv_id":"2406.00212","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-point-neighborhood-learning-framework-for","title":"A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation","date":"2024-05-30","arxiv_id":"2405.20044","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-the-accuracy-bias-by-facial-hairstyle-be","title":"Can the accuracy bias by facial hairstyle be reduced through balancing the training data?","date":"2024-05-30","arxiv_id":"2405.20062","repositories_listed":0,"syntology":null},{"url":null,"slug":"facemixup-enhancing-facial-expression","title":"FaceMixup: Enhancing Facial Expression Recognition through Mixed Face Regularization","date":"2024-05-30","arxiv_id":"2405.20259","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-object-detector-training-on","title":"Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology","date":"2024-05-30","arxiv_id":"2405.19822","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-smote-via-fusing-conditional-vae","title":"Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering","date":"2024-05-30","arxiv_id":"2405.19757","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-the-impact-of-labeling-errors-on","title":"Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation","date":"2024-05-30","arxiv_id":"2405.20531","repositories_listed":0,"syntology":null},{"url":null,"slug":"pga-scire-harnessing-llm-on-data-augmentation","title":"PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction","date":"2024-05-30","arxiv_id":"2405.20787","repositories_listed":0,"syntology":null},{"url":null,"slug":"symmetries-in-overparametrized-neural","title":"Symmetries in Overparametrized Neural Networks: A Mean-Field View","date":"2024-05-30","arxiv_id":"2405.19995","repositories_listed":0,"syntology":null},{"url":null,"slug":"entprop-high-entropy-propagation-for","title":"EntProp: High Entropy Propagation for Improving Accuracy and Robustness","date":"2024-05-29","arxiv_id":"2405.18931","repositories_listed":0,"syntology":null},{"url":null,"slug":"eventzoom-a-progressive-approach-to-event","title":"EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision","date":"2024-05-29","arxiv_id":"2405.18880","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-generative-ai-for-smart-city","title":"Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement","date":"2024-05-29","arxiv_id":"2405.19464","repositories_listed":0,"syntology":null},{"url":null,"slug":"arithmetic-reasoning-with-llm-prolog","title":"Arithmetic Reasoning with LLM: Prolog Generation & Permutation","date":"2024-05-28","arxiv_id":"2405.17893","repositories_listed":0,"syntology":null},{"url":"/paper/mitigating-object-hallucination-via-data","slug":"mitigating-object-hallucination-via-data","title":"Data-augmented phrase-level alignment for mitigating object hallucination","date":"2024-05-28","arxiv_id":"2405.18654","repositories_listed":0,"syntology":{"n":10,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":10,"phrase":"6 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; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/mitigating-object-hallucination-via-data#ran","syntology_url":"https://syntology.ai/paper/2405.18654","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.18654"}},"official":null}},{"url":null,"slug":"notplanet-removing-false-positives-from","title":"NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning","date":"2024-05-28","arxiv_id":"2405.18278","repositories_listed":0,"syntology":null},{"url":"/paper/pursuing-feature-separation-based-on-neural","slug":"pursuing-feature-separation-based-on-neural","title":"Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection","date":"2024-05-28","arxiv_id":"2405.17816","repositories_listed":0,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pursuing-feature-separation-based-on-neural#ran","syntology_url":"https://syntology.ai/paper/2405.17816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.17816"}},"official":null}},{"url":null,"slug":"rc-mixup-a-data-augmentation-strategy-against","title":"RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks","date":"2024-05-28","arxiv_id":"2405.17938","repositories_listed":0,"syntology":null},{"url":null,"slug":"tripletmix-triplet-data-augmentation-for-3d","title":"MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding","date":"2024-05-28","arxiv_id":"2405.18523","repositories_listed":0,"syntology":null},{"url":null,"slug":"dualcontrast-unsupervised-disentangling-of","title":"DualContrast: Unsupervised Disentangling of Content and Transformations with Implicit Parameterization","date":"2024-05-27","arxiv_id":"2405.16796","repositories_listed":0,"syntology":null},{"url":null,"slug":"acceleration-of-grokking-in-learning","title":"Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation","date":"2024-05-26","arxiv_id":"2405.16658","repositories_listed":0,"syntology":null},{"url":null,"slug":"dominant-shuffle-a-simple-yet-powerful-data","title":"Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction","date":"2024-05-26","arxiv_id":"2405.16456","repositories_listed":0,"syntology":null},{"url":null,"slug":"certifying-adapters-enabling-and-enhancing","title":"Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness","date":"2024-05-25","arxiv_id":"2405.16036","repositories_listed":0,"syntology":null},{"url":null,"slug":"treeformers-an-exploration-of-vision","title":"TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification","date":"2024-05-25","arxiv_id":"2405.15989","repositories_listed":0,"syntology":null},{"url":null,"slug":"free-performance-gain-from-mixing-multiple","title":"Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification","date":"2024-05-24","arxiv_id":"2405.15860","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-in-the-loop-reinforcement-learning-for","title":"Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments","date":"2024-05-24","arxiv_id":"2405.15508","repositories_listed":0,"syntology":null},{"url":null,"slug":"planted-a-dataset-for-planted-forest","title":"Planted: a dataset for planted forest identification from multi-satellite time series","date":"2024-05-24","arxiv_id":"2406.18554","repositories_listed":0,"syntology":null},{"url":null,"slug":"unbiased-faster-r-cnn-for-single-source","title":"Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection","date":"2024-05-24","arxiv_id":"2405.15225","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-euclidean-alignment-and-data","title":"Combining Euclidean Alignment and Data Augmentation for BCI decoding","date":"2024-05-23","arxiv_id":"2405.14994","repositories_listed":0,"syntology":null},{"url":null,"slug":"configuring-data-augmentations-to-reduce","title":"Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers","date":"2024-05-23","arxiv_id":"2405.14115","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-method-utilizing-template","title":"Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction","date":"2024-05-23","arxiv_id":"2405.14962","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-techniques-for-process","title":"Data Augmentation Techniques for Process Extraction from Scientific Publications","date":"2024-05-23","arxiv_id":"2405.14594","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-specific-augmentations-with-resolution","title":"Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images","date":"2024-05-23","arxiv_id":"2405.14453","repositories_listed":0,"syntology":null},{"url":null,"slug":"flooddamagecast-building-flood-damage","title":"FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation","date":"2024-05-23","arxiv_id":"2405.14232","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-language-models-trained-with","title":"Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis","date":"2024-05-23","arxiv_id":"2405.14277","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-generation-for-intersectional","title":"Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure","date":"2024-05-23","arxiv_id":"2405.14521","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-label-propagation-strategy-for-cutmix-in","title":"A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification","date":"2024-05-22","arxiv_id":"2405.13451","repositories_listed":0,"syntology":null},{"url":null,"slug":"kpg-key-propagation-graph-generator-for-rumor","title":"Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator","date":"2024-05-21","arxiv_id":"2405.13094","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-the-explainability-and-generalization","title":"Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction","date":"2024-05-21","arxiv_id":"2405.12579","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-text-based-person","title":"Data Augmentation for Text-based Person Retrieval Using Large Language Models","date":"2024-05-20","arxiv_id":"2405.11971","repositories_listed":0,"syntology":null}],"record_sha256":"1d3d5e3d7eb01e788732e8cdb4235018a5e46967666bb32b310a8613d62064e3","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}