{"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/33","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":33,"pages_in_order":84,"rows_per_page":100,"rows":[3201,3300],"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/32","next":"/task/data-augmentation/papers/34","papers":[{"url":"/paper/learning-6-dof-grasping-interaction-via-deep","slug":"learning-6-dof-grasping-interaction-via-deep","title":"Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations","date":"2017-08-24","arxiv_id":"1708.07303","repositories_listed":1,"syntology":null},{"url":"/paper/uw-finsent-at-semeval-2017-task-5-sentiment","slug":"uw-finsent-at-semeval-2017-task-5-sentiment","title":"UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/analysis-and-optimization-of-convolutional","slug":"analysis-and-optimization-of-convolutional","title":"Analysis and Optimization of Convolutional Neural Network Architectures","date":"2017-07-31","arxiv_id":"1707.09725","repositories_listed":1,"syntology":null},{"url":"/paper/sentiment-analysis-on-financial-news","slug":"sentiment-analysis-on-financial-news","title":"Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation","date":"2017-07-29","arxiv_id":"1707.09448","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automatic-and-real-time-catheter","slug":"fully-automatic-and-real-time-catheter","title":"Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy","date":"2017-07-17","arxiv_id":"1707.05137","repositories_listed":1,"syntology":null},{"url":"/paper/improving-lstm-ctc-based-asr-performance-in","slug":"improving-lstm-ctc-based-asr-performance-in","title":"Improving LSTM-CTC based ASR performance in domains with limited training data","date":"2017-07-03","arxiv_id":"1707.00722","repositories_listed":1,"syntology":null},{"url":"/paper/aga-attribute-guided-augmentation-1","slug":"aga-attribute-guided-augmentation-1","title":"AGA: Attribute-Guided Augmentation","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-for-low-resource-neural","slug":"data-augmentation-for-low-resource-neural","title":"Data Augmentation for Low-Resource Neural Machine Translation","date":"2017-05-01","arxiv_id":"1705.00440","repositories_listed":1,"syntology":null},{"url":"/paper/whats-in-a-question-using-visual-questions-as","slug":"whats-in-a-question-using-visual-questions-as","title":"What's in a Question: Using Visual Questions as a Form of Supervision","date":"2017-04-12","arxiv_id":"1704.03895","repositories_listed":1,"syntology":null},{"url":"/paper/deep-convolutional-neural-networks-and-data-2","slug":"deep-convolutional-neural-networks-and-data-2","title":"Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification","date":"2017-01-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/aenet-learning-deep-audio-features-for-video","slug":"aenet-learning-deep-audio-features-for-video","title":"AENet: Learning Deep Audio Features for Video Analysis","date":"2017-01-03","arxiv_id":"1701.00599","repositories_listed":1,"syntology":null},{"url":"/paper/harmonic-networks-deep-translation-and","slug":"harmonic-networks-deep-translation-and","title":"Harmonic Networks: Deep Translation and Rotation Equivariance","date":"2016-12-14","arxiv_id":"1612.04642","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/harmonic-networks-deep-translation-and#ran","syntology_url":"https://syntology.ai/paper/1612.04642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.04642"}},"official":null}},{"url":"/paper/aga-attribute-guided-augmentation","slug":"aga-attribute-guided-augmentation","title":"AGA: Attribute Guided Augmentation","date":"2016-12-08","arxiv_id":"1612.02559","repositories_listed":1,"syntology":null},{"url":"/paper/breast-mass-classification-from-mammograms","slug":"breast-mass-classification-from-mammograms","title":"Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks","date":"2016-12-02","arxiv_id":"1612.00542","repositories_listed":1,"syntology":null},{"url":"/paper/variational-bayes-in-private-settings-vips","slug":"variational-bayes-in-private-settings-vips","title":"Variational Bayes In Private Settings (VIPS)","date":"2016-11-01","arxiv_id":"1611.00340","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-optimization-with-variance","slug":"stochastic-optimization-with-variance","title":"Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure","date":"2016-10-04","arxiv_id":"1610.00970","repositories_listed":1,"syntology":null},{"url":"/paper/generating-synthetic-data-for-text","slug":"generating-synthetic-data-for-text","title":"Generating Synthetic Data for Text Recognition","date":"2016-08-15","arxiv_id":"1608.04224","repositories_listed":1,"syntology":null},{"url":"/paper/adjusting-for-dropout-variance-in-batch","slug":"adjusting-for-dropout-variance-in-batch","title":"Adjusting for Dropout Variance in Batch Normalization and Weight Initialization","date":"2016-07-08","arxiv_id":"1607.02488","repositories_listed":1,"syntology":null},{"url":"/paper/character-level-question-answering-with","slug":"character-level-question-answering-with","title":"Character-Level Question Answering with Attention","date":"2016-04-04","arxiv_id":"1604.00727","repositories_listed":1,"syntology":null},{"url":"/paper/image-captioning-with-deep-bidirectional","slug":"image-captioning-with-deep-bidirectional","title":"Image Captioning with Deep Bidirectional LSTMs","date":"2016-04-04","arxiv_id":"1604.00790","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-via-levy-processes","slug":"data-augmentation-via-levy-processes","title":"Data Augmentation via Levy Processes","date":"2016-03-21","arxiv_id":"1603.06340","repositories_listed":1,"syntology":null},{"url":"/paper/anatomy-specific-classification-of-medical","slug":"anatomy-specific-classification-of-medical","title":"Anatomy-specific classification of medical images using deep convolutional nets","date":"2015-04-15","arxiv_id":"1504.04003","repositories_listed":1,"syntology":null},{"url":"/paper/invariant-backpropagation-how-to-train-a","slug":"invariant-backpropagation-how-to-train-a","title":"Invariant backpropagation: how to train a transformation-invariant neural network","date":"2015-02-16","arxiv_id":"1502.04434","repositories_listed":1,"syntology":null},{"url":"/paper/return-of-the-devil-in-the-details-delving","slug":"return-of-the-devil-in-the-details-delving","title":"Return of the Devil in the Details: Delving Deep into Convolutional Nets","date":"2014-05-14","arxiv_id":"1405.3531","repositories_listed":1,"syntology":null},{"url":"/paper/stochastic-pooling-for-regularization-of-deep","slug":"stochastic-pooling-for-regularization-of-deep","title":"Stochastic Pooling for Regularization of Deep Convolutional Neural Networks","date":"2013-01-16","arxiv_id":"1301.3557","repositories_listed":1,"syntology":null},{"url":null,"slug":"overview-of-the-talentclef-2025-skill-and-job","title":"Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management","date":"2025-07-17","arxiv_id":"2507.13275","repositories_listed":0,"syntology":null},{"url":null,"slug":"pixel-perfect-megamed-a-megapixel-scale","title":"Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images","date":"2025-07-17","arxiv_id":"2507.12698","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-guided-diffusion-for-contrastive","title":"Similarity-Guided Diffusion for Contrastive Sequential Recommendation","date":"2025-07-16","arxiv_id":"2507.11866","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-in-time-series-forecasting","title":"Data Augmentation in Time Series Forecasting through Inverted Framework","date":"2025-07-15","arxiv_id":"2507.11439","repositories_listed":0,"syntology":null},{"url":null,"slug":"iceberg-enhancing-hls-modeling-with-synthetic","title":"Iceberg: Enhancing HLS Modeling with Synthetic Data","date":"2025-07-14","arxiv_id":"2507.09948","repositories_listed":0,"syntology":null},{"url":null,"slug":"freeaudio-training-free-timing-planning-for","title":"FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation","date":"2025-07-11","arxiv_id":"2507.08557","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-offline-handwritten-text","title":"Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques","date":"2025-07-08","arxiv_id":"2507.06275","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolution-without-large-models-training","title":"Evolution without Large Models: Training Language Model with Task Principles","date":"2025-07-08","arxiv_id":"2507.05991","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-certainty-assessment-in-vector","title":"Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation","date":"2025-07-08","arxiv_id":"2507.05933","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-quality-assessment-model-based-on","title":"Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis","date":"2025-07-08","arxiv_id":"2507.06116","repositories_listed":0,"syntology":null},{"url":null,"slug":"tigaug-data-augmentation-for-testing-traffic","title":"TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems","date":"2025-07-08","arxiv_id":"2507.05932","repositories_listed":0,"syntology":null},{"url":null,"slug":"piggyback-camera-easy-to-deploy-visual","title":"Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums","date":"2025-07-07","arxiv_id":"2507.04910","repositories_listed":0,"syntology":null},{"url":null,"slug":"diffusion-model-based-data-augmentation","title":"Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation","date":"2025-06-30","arxiv_id":"2506.23664","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybridq-hybrid-classical-quantum-generative","title":"HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation","date":"2025-06-26","arxiv_id":"2506.21015","repositories_listed":0,"syntology":null},{"url":null,"slug":"unlasting-unpaired-single-cell-multi","title":"Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges","date":"2025-06-26","arxiv_id":"2506.21107","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-ambiguous-dynamic-facial-expression","title":"Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation","date":"2025-06-25","arxiv_id":"2506.20867","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-ai-graders-for-missing-score","title":"Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests","date":"2025-06-25","arxiv_id":"2506.20119","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-regularization-adaptive-model","title":"Cross-regularization: Adaptive Model Complexity through Validation Gradients","date":"2025-06-24","arxiv_id":"2506.19755","repositories_listed":0,"syntology":null},{"url":null,"slug":"harpt-a-corpus-for-analyzing-consumers-trust","title":"HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps","date":"2025-06-24","arxiv_id":"2506.19268","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-assisted-photonic-device","title":"Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization","date":"2025-06-24","arxiv_id":"2506.20056","repositories_listed":0,"syntology":null},{"url":null,"slug":"dro-augment-framework-robustness-by","title":"DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation","date":"2025-06-22","arxiv_id":"2506.17874","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-temporal-positional-encodings-for","title":"Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT","date":"2025-06-22","arxiv_id":"2506.18114","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-robustness-of-human-object-interaction","title":"On the Robustness of Human-Object Interaction Detection against Distribution Shift","date":"2025-06-22","arxiv_id":"2506.18021","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-training-with-data-augmentation-for","title":"Robust Training with Data Augmentation for Medical Imaging Classification","date":"2025-06-20","arxiv_id":"2506.17133","repositories_listed":0,"syntology":null},{"url":null,"slug":"copulasmote-a-copula-based-oversampling","title":"CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction","date":"2025-06-18","arxiv_id":"2506.17326","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-data-augmentation-for-thompson","title":"Adaptive Data Augmentation for Thompson Sampling","date":"2025-06-17","arxiv_id":"2506.14479","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-attribute-imbalance-in-vision","title":"Compositional Attribute Imbalance in Vision Datasets","date":"2025-06-17","arxiv_id":"2506.14418","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-detection-of-implicit-influential","title":"Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation","date":"2025-06-17","arxiv_id":"2506.14211","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-non-contrastive-self-supervised","title":"Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling","date":"2025-06-17","arxiv_id":"2506.14265","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-compression-using-knowledge","title":"Model compression using knowledge distillation with integrated gradients","date":"2025-06-17","arxiv_id":"2506.14440","repositories_listed":0,"syntology":null},{"url":null,"slug":"organ-a-synthetic-data-augmentation-pipeline","title":"orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels","date":"2025-06-17","arxiv_id":"2506.14303","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-data-augmentation-for-table","title":"Synthetic Data Augmentation for Table Detection: Re-evaluating TableNet's Performance with Automatically Generated Document Images","date":"2025-06-17","arxiv_id":"2506.14583","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-perception-of-phase-intercept-distortion","title":"The Perception of Phase Intercept Distortion and its Application in Data Augmentation","date":"2025-06-17","arxiv_id":"2506.14571","repositories_listed":0,"syntology":null},{"url":null,"slug":"thinking-in-directivity-speech-large-language","title":"Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition","date":"2025-06-17","arxiv_id":"2506.14973","repositories_listed":0,"syntology":null},{"url":null,"slug":"asmr-augmenting-life-scenario-using-large","title":"ASMR: Augmenting Life Scenario using Large Generative Models for Robotic Action Reflection","date":"2025-06-16","arxiv_id":"2506.13956","repositories_listed":0,"syntology":null},{"url":null,"slug":"blastdiffusion-a-latent-diffusion-model-for","title":"BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization","date":"2025-06-16","arxiv_id":"2506.13843","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-diffusion-models-and-unsupervised","title":"Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis","date":"2025-06-16","arxiv_id":"2506.13484","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-beta-vae-for-synthetic","title":"Graph-Convolutional-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation","date":"2025-06-16","arxiv_id":"2506.13628","repositories_listed":0,"syntology":null},{"url":null,"slug":"multivit2-a-data-augmented-multimodal","title":"MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model","date":"2025-06-16","arxiv_id":"2506.13667","repositories_listed":0,"syntology":null},{"url":null,"slug":"pro-projection-domain-synthesis-for-ct","title":"PRO: Projection Domain Synthesis for CT Imaging","date":"2025-06-16","arxiv_id":"2506.13443","repositories_listed":0,"syntology":null},{"url":null,"slug":"seewo-s-submission-to-mlc-slm-lessons-learned","title":"Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models","date":"2025-06-16","arxiv_id":"2506.13300","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-learning-invariance-in-deep","title":"Understanding Learning Invariance in Deep Linear Networks","date":"2025-06-16","arxiv_id":"2506.13714","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10401","title":"HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration","date":"2025-06-12","arxiv_id":"2506.10401","repositories_listed":0,"syntology":null},{"url":null,"slug":"dreamactor-h1-high-fidelity-human-product","title":"DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers","date":"2025-06-12","arxiv_id":"2506.10568","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-adapting-language-models","title":"Self-Adapting Language Models","date":"2025-06-12","arxiv_id":"2506.10943","repositories_listed":0,"syntology":null},{"url":null,"slug":"alzheimer-s-dementia-detection-using","title":"Alzheimer's Dementia Detection Using Perplexity from Paired Large Language Models","date":"2025-06-11","arxiv_id":"2506.09315","repositories_listed":0,"syntology":null},{"url":null,"slug":"scoremix-improving-face-recognition-via-score","title":"ScoreMix: Improving Face Recognition via Score Composition in Diffusion Generators","date":"2025-06-11","arxiv_id":"2506.10226","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-explainable-deep-learning-framework-for","title":"An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation","date":"2025-06-10","arxiv_id":"2506.09161","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-small-object-using-fast","title":"Data Augmentation For Small Object using Fast AutoAugment","date":"2025-06-10","arxiv_id":"2506.08956","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-efficient-challenges-in-visual-inductive","title":"Data-Efficient Challenges in Visual Inductive Priors: A Retrospective","date":"2025-06-10","arxiv_id":"2506.08612","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-hear-broken-motors-signature","title":"Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics","date":"2025-06-10","arxiv_id":"2506.08412","repositories_listed":0,"syntology":null},{"url":null,"slug":"simclass-a-classroom-speech-dataset-generated","title":"SimClass: A Classroom Speech Dataset Generated via Game Engine Simulation For Automatic Speech Recognition Research","date":"2025-06-10","arxiv_id":"2506.09206","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-deep-learning-models-for","title":"Spatiotemporal deep learning models for detection of rapid intensification in cyclones","date":"2025-06-10","arxiv_id":"2506.08397","repositories_listed":0,"syntology":null},{"url":null,"slug":"dealing-with-the-evil-twins-improving-random","title":"Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations","date":"2025-06-09","arxiv_id":"2506.08240","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepvideo-r1-video-reinforcement-fine-tuning","title":"DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO","date":"2025-06-09","arxiv_id":"2506.07464","repositories_listed":0,"syntology":null},{"url":null,"slug":"scaling-human-activity-recognition-a","title":"Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques","date":"2025-06-09","arxiv_id":"2506.07612","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-inertial-pose-a-deep-learning-approach","title":"Deep Inertial Pose: A deep learning approach for human pose estimation","date":"2025-06-07","arxiv_id":"2506.06850","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-sensor-fusion-against-on-vehicle","title":"Robust sensor fusion against on-vehicle sensor staleness","date":"2025-06-06","arxiv_id":"2506.05780","repositories_listed":0,"syntology":null},{"url":null,"slug":"securing-traffic-sign-recognition-systems-in","title":"Securing Traffic Sign Recognition Systems in Autonomous Vehicles","date":"2025-06-06","arxiv_id":"2506.06563","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-04714","title":"IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation","date":"2025-06-05","arxiv_id":"2506.04714","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-05127","title":"PixCell: A generative foundation model for digital histopathology images","date":"2025-06-05","arxiv_id":"2506.05127","repositories_listed":0,"syntology":null},{"url":null,"slug":"geometric-and-physical-constraints","title":"Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates","date":"2025-06-05","arxiv_id":"2506.05513","repositories_listed":0,"syntology":null},{"url":null,"slug":"hdl2v-a-code-translation-dataset-for-enhanced","title":"hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation","date":"2025-06-05","arxiv_id":"2506.04544","repositories_listed":0,"syntology":null},{"url":null,"slug":"llm-based-phoneme-to-grapheme-for-phoneme","title":"LLM-based phoneme-to-grapheme for phoneme-based speech recognition","date":"2025-06-05","arxiv_id":"2506.04711","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-based-neural-data-augmentation-for-sub","title":"Model-based Neural Data Augmentation for sub-wavelength Radio Localization","date":"2025-06-05","arxiv_id":"2506.06387","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-data-augmentation-approach-for","title":"A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions","date":"2025-06-04","arxiv_id":"2506.04077","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-video-transformers-for-word-level","title":"Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks","date":"2025-06-04","arxiv_id":"2506.04367","repositories_listed":0,"syntology":null},{"url":null,"slug":"person-re-identification-system-at-semantic","title":"Person Re-Identification System at Semantic Level based on Pedestrian Attributes Ontology","date":"2025-06-04","arxiv_id":"2506.04143","repositories_listed":0,"syntology":null},{"url":null,"slug":"explicitly-modeling-subcortical-vision-with-a","title":"Explicitly Modeling Subcortical Vision with a Neuro-Inspired Front-End Improves CNN Robustness","date":"2025-06-03","arxiv_id":"2506.03089","repositories_listed":0,"syntology":null},{"url":null,"slug":"master-enhancing-large-language-model-via","title":"MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching","date":"2025-06-03","arxiv_id":"2506.02689","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-skeleton-based-action-recognition-a-review","title":"3D Skeleton-Based Action Recognition: A Review","date":"2025-06-01","arxiv_id":"2506.00915","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-flat-minima-perspective-on-understanding","title":"A Flat Minima Perspective on Understanding Augmentations and Model Robustness","date":"2025-05-30","arxiv_id":"2505.24592","repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-automatic-exercise-evaluation","title":"Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation","date":"2025-05-30","arxiv_id":"2505.24415","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-multilingual-speech-models-on-ml","title":"Improving Multilingual Speech Models on ML-SUPERB 2.0: Fine-tuning with Data Augmentation and LID-Aware CTC","date":"2025-05-30","arxiv_id":"2505.24200","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-intermediate-features-of-vision","title":"Leveraging Intermediate Features of Vision Transformer for Face Anti-Spoofing","date":"2025-05-30","arxiv_id":"2505.24402","repositories_listed":0,"syntology":null}],"record_sha256":"08e10f6335c10dffef7fe2e48a628bfed3c8bf4f888d48ff890f86eccc29eefa","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}