{"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/82","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":82,"pages_in_order":84,"rows_per_page":100,"rows":[8101,8200],"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/81","next":"/task/data-augmentation/papers/83","papers":[{"url":null,"slug":"acoustic-and-textual-data-augmentation-for","title":"Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech","date":"2018-07-28","arxiv_id":"1807.10945","repositories_listed":0,"syntology":null},{"url":null,"slug":"back-translation-style-data-augmentation-for","title":"Back-Translation-Style Data Augmentation for End-to-End ASR","date":"2018-07-28","arxiv_id":"1807.10893","repositories_listed":0,"syntology":null},{"url":null,"slug":"characters-detection-on-namecard-with-faster","title":"Characters Detection on Namecard with faster RCNN","date":"2018-07-27","arxiv_id":"1807.10417","repositories_listed":0,"syntology":null},{"url":null,"slug":"region-of-interest-detection-in-dermoscopic","title":"Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images","date":"2018-07-27","arxiv_id":"1807.10711","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-image-synthesis-for-data-augmentation","title":"Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks","date":"2018-07-26","arxiv_id":"1807.10225","repositories_listed":0,"syntology":null},{"url":null,"slug":"isic-2017-skin-lesion-segmentation-using-deep","title":"ISIC 2017 Skin Lesion Segmentation Using Deep Encoder-Decoder Network","date":"2018-07-24","arxiv_id":"1807.09083","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-speech-recognition-for-humanitarian","title":"Automatic Speech Recognition for Humanitarian Applications in Somali","date":"2018-07-23","arxiv_id":"1807.08669","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-automatic-skin-lesion-segmentation","title":"Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation","date":"2018-07-23","arxiv_id":"1807.08392","repositories_listed":0,"syntology":null},{"url":null,"slug":"bio-measurements-estimation-and-support-in","title":"Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning","date":"2018-07-19","arxiv_id":"1807.07521","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-noise-invariant-representations-for","title":"Learning Noise-Invariant Representations for Robust Speech Recognition","date":"2018-07-17","arxiv_id":"1807.06610","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-deep-multi-modal-learning-based-on","title":"Robust Deep Multi-modal Learning Based on Gated Information Fusion Network","date":"2018-07-17","arxiv_id":"1807.06233","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-network-ensemble-by-data","title":"Deep neural network ensemble by data augmentation and bagging for skin lesion classification","date":"2018-07-15","arxiv_id":"1807.05496","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-feature-learning-for","title":"Semi-supervised Feature Learning For Improving Writer Identification","date":"2018-07-15","arxiv_id":"1807.05490","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-semi-supervised-segmentation-with-weight","title":"Deep semi-supervised segmentation with weight-averaged consistency targets","date":"2018-07-12","arxiv_id":"1807.04657","repositories_listed":0,"syntology":null},{"url":null,"slug":"hydranet-data-augmentation-for-regression","title":"Hydranet: Data Augmentation for Regression Neural Networks","date":"2018-07-12","arxiv_id":"1807.04798","repositories_listed":0,"syntology":null},{"url":null,"slug":"subsampled-turbulence-removal-network","title":"Subsampled Turbulence Removal Network","date":"2018-07-12","arxiv_id":"1807.04418","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-hyperspectral-image","title":"Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations","date":"2018-07-11","arxiv_id":"1807.10574","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-resolution-mammogram-synthesis-using","title":"High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks","date":"2018-07-09","arxiv_id":"1807.03401","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-detection-of","title":"Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach","date":"2018-07-06","arxiv_id":"1807.03167","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamlet-hierarchical-harmonic-filters-for","title":"HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI","date":"2018-07-03","arxiv_id":"1807.01068","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-under-selective-labels-in-the","title":"Learning under selective labels in the presence of expert consistency","date":"2018-07-02","arxiv_id":"1807.00905","repositories_listed":0,"syntology":null},{"url":null,"slug":"cmmc-bdrc-solution-to-the-nlp-tea-2018","title":"CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dependent-relational-gamma-process-models-for","title":"Dependent Relational Gamma Process Models for Longitudinal Networks","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-from-mask-a-deep-learning-based-human","title":"Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images","date":"2018-06-22","arxiv_id":"1806.08485","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-brain-computer-interface","title":"Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks","date":"2018-06-19","arxiv_id":"1806.07108","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-attend-and-translate-unsupervised-image","title":"Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention","date":"2018-06-16","arxiv_id":"1806.06195","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-enhancement-augmentation-and","title":"A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition","date":"2018-06-13","arxiv_id":"1806.04841","repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-dropout-for-audio-scene-classification","title":"Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network","date":"2018-06-12","arxiv_id":"1806.04422","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantically-selective-augmentation-for-deep","title":"Semantically Selective Augmentation for Deep Compact Person Re-Identification","date":"2018-06-11","arxiv_id":"1806.04074","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformationally-identical-and-invariant-1","title":"Transformationally Identical and Invariant Convolutional Neural Networks through Symmetric Element Operators","date":"2018-06-10","arxiv_id":"1806.03636","repositories_listed":0,"syntology":null},{"url":null,"slug":"findings-of-the-second-workshop-on-neural","title":"Findings of the Second Workshop on Neural Machine Translation and Generation","date":"2018-06-08","arxiv_id":"1806.02940","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-stochastic-optimization-for","title":"Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data","date":"2018-06-08","arxiv_id":"1806.02927","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-networks-for-realistic","title":"Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples","date":"2018-06-07","arxiv_id":"1806.02583","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-augmentation-with-adversarial","title":"Training Augmentation with Adversarial Examples for Robust Speech Recognition","date":"2018-06-07","arxiv_id":"1806.02782","repositories_listed":0,"syntology":null},{"url":null,"slug":"study-and-development-of-a-computer-aided","title":"Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation","date":"2018-06-03","arxiv_id":"1806.00839","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-pyramid-pooling-and-attention","title":"Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion","date":"2018-06-01","arxiv_id":"1806.00264","repositories_listed":0,"syntology":null},{"url":null,"slug":"da-gan-instance-level-image-translation-by-1","title":"DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lyb3b-at-semeval-2018-task-11-machine","title":"Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mit-medg-at-semeval-2018-task-7-semantic","title":"MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multistage-adversarial-losses-for-pose-based","title":"Multistage Adversarial Losses for Pose-Based Human Image Synthesis","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pose-guided-photorealistic-face-rotation","title":"Pose-Guided Photorealistic Face Rotation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-for-jointly-learning-to-ask-and","title":"Self-Training for Jointly Learning to Ask and Answer Questions","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-paraphrasing-and-memory-augmented","title":"Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-pedestrian-localization-in-overhead","title":"Accurate pedestrian localization in overhead depth images via Height-Augmented HOG","date":"2018-05-31","arxiv_id":"1805.12510","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-time-predictive-modeling-of-nonlinear","title":"Long-time predictive modeling of nonlinear dynamical systems using neural networks","date":"2018-05-31","arxiv_id":"1805.12547","repositories_listed":0,"syntology":null},{"url":null,"slug":"capturing-variabilities-from-computed","title":"Capturing Variabilities from Computed Tomography Images with Generative Adversarial Networks","date":"2018-05-29","arxiv_id":"1805.11504","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-data-augmentation-for-brain-tumor","title":"Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks","date":"2018-05-29","arxiv_id":"1805.11291","repositories_listed":0,"syntology":null},{"url":null,"slug":"transductive-label-augmentation-for-improved","title":"Transductive Label Augmentation for Improved Deep Network Learning","date":"2018-05-26","arxiv_id":"1805.10546","repositories_listed":0,"syntology":null},{"url":"/paper/jointly-optimize-data-augmentation-and","slug":"jointly-optimize-data-augmentation-and","title":"Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation","date":"2018-05-24","arxiv_id":"1805.09707","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-nonlinear-brain-dynamics-van-der-pol","title":"Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM","date":"2018-05-24","arxiv_id":"1805.09874","repositories_listed":0,"syntology":null},{"url":null,"slug":"input-and-weight-space-smoothing-for-semi","title":"Input and Weight Space Smoothing for Semi-supervised Learning","date":"2018-05-23","arxiv_id":"1805.09302","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-high-quality-surface-realizations","title":"Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models","date":"2018-05-20","arxiv_id":"1805.07731","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-for-patient-independent","title":"Adversarial Training for Patient-Independent Feature Learning with IVOCT Data for Plaque Classification","date":"2018-05-16","arxiv_id":"1805.06223","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-affinity-based-pseudo-labeling-for","title":"Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification","date":"2018-05-16","arxiv_id":"1805.06118","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-transformer-introspective-neural","title":"Resisting Large Data Variations via Introspective Transformation Network","date":"2018-05-16","arxiv_id":"1805.06447","repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-and-controlling-user","title":"Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning","date":"2018-05-15","arxiv_id":"1805.05838","repositories_listed":0,"syntology":null},{"url":null,"slug":"stingray-detection-of-aerial-images-using","title":"Stingray Detection of Aerial Images Using Augmented Training Images Generated by A Conditional Generative Model","date":"2018-05-11","arxiv_id":"1805.04262","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-domain-sensitive-and-sentiment-aware","title":"Learning Domain-Sensitive and Sentiment-Aware Word Embeddings","date":"2018-05-10","arxiv_id":"1805.03801","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-ordinal-classification-and","title":"Image Ordinal Classification and Understanding: Grid Dropout with Masking Label","date":"2018-05-08","arxiv_id":"1805.02901","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmenting-image-question-answering-dataset","title":"Augmenting Image Question Answering Dataset by Exploiting Image Captions","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-jslc-a-multimodal-corpus-collection-for","title":"Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-feature-space-speaker","title":"Evaluation of Feature-Space Speaker Adaptation for End-to-End Acoustic Models","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"handling-rare-word-problem-using-synthetic","title":"Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-asr-errors-for-training-slu","title":"Simulating ASR errors for training SLU systems","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simulating-dysarthric-speech-for-training","title":"Simulating dysarthric speech for training data augmentation in clinical speech applications","date":"2018-04-27","arxiv_id":"1804.10325","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-speech-recognition-for-launch","title":"Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model","date":"2018-04-24","arxiv_id":"1804.09552","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-learning-of-dynamic-gestures","title":"Spatiotemporal Learning of Dynamic Gestures from 3D Point Cloud Data","date":"2018-04-24","arxiv_id":"1804.08859","repositories_listed":0,"syntology":null},{"url":"/paper/mvtec-d2s-densely-segmented-supermarket","slug":"mvtec-d2s-densely-segmented-supermarket","title":"MVTec D2S: Densely Segmented Supermarket Dataset","date":"2018-04-23","arxiv_id":"1804.08292","repositories_listed":0,"syntology":null},{"url":"/paper/learning-to-refine-human-pose-estimation","slug":"learning-to-refine-human-pose-estimation","title":"Learning to Refine Human Pose Estimation","date":"2018-04-21","arxiv_id":"1804.07909","repositories_listed":0,"syntology":null},{"url":null,"slug":"fortification-of-neural-morphological","title":"Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages","date":"2018-04-17","arxiv_id":"1804.06024","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-machine-comprehension-models-via","title":"Robust Machine Comprehension Models via Adversarial Training","date":"2018-04-17","arxiv_id":"1804.06473","repositories_listed":0,"syntology":null},{"url":null,"slug":"building-robust-prediction-models-for","title":"Building robust prediction models for defective sensor data using Artificial Neural Networks","date":"2018-04-16","arxiv_id":"1804.05544","repositories_listed":0,"syntology":null},{"url":null,"slug":"transcribing-lyrics-from-commercial-song","title":"Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing","date":"2018-04-15","arxiv_id":"1804.05306","repositories_listed":0,"syntology":null},{"url":null,"slug":"3d-g-cnns-for-pulmonary-nodule-detection","title":"3D G-CNNs for Pulmonary Nodule Detection","date":"2018-04-12","arxiv_id":"1804.04656","repositories_listed":0,"syntology":null},{"url":null,"slug":"e-commerce-anomaly-detection-a-bayesian-semi","title":"E-commerce Anomaly Detection: A Bayesian Semi-Supervised Tensor Decomposition Approach using Natural Gradients","date":"2018-04-11","arxiv_id":"1804.03836","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-kernel-to-solve-nearly-everything-unified","title":"One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis","date":"2018-04-11","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"real-world-plant-species-identification-based","title":"Real-world plant species identification based on deep convolutional neural networks and visual attention","date":"2018-04-11","arxiv_id":"1804.03853","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-ultrasound-image-reconstruction","title":"Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning","date":"2018-04-06","arxiv_id":"1804.02119","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-robustness-of-speech-emotion","title":"On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks","date":"2018-04-06","arxiv_id":"1804.02173","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-deep-metrics-for-image","title":"Semi-Supervised Deep Metrics for Image Registration","date":"2018-04-04","arxiv_id":"1804.01565","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-learning-for-spectrum","title":"Generative Adversarial Learning for Spectrum Sensing","date":"2018-04-02","arxiv_id":"1804.00709","repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-challenging-handwritten","title":"Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks","date":"2018-04-01","arxiv_id":"1804.00236","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-loss-functions-and-deep-densely","title":"Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection","date":"2018-03-28","arxiv_id":"1803.11078","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-data-augmentation-for-end-to-end","title":"Multi-Modal Data Augmentation for End-to-End ASR","date":"2018-03-27","arxiv_id":"1803.10299","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-highly-accurate-coral-texture-images","title":"Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation","date":"2018-03-27","arxiv_id":"1804.00516","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-localization-function-machine","title":"Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization","date":"2018-03-21","arxiv_id":"1803.08153","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerial-lanenet-lane-marking-semantic","title":"Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks","date":"2018-03-19","arxiv_id":"1803.06904","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-kernel-theory-of-modern-data-augmentation","title":"A Kernel Theory of Modern Data Augmentation","date":"2018-03-16","arxiv_id":"1803.06084","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-learning-algorithm-for-one-step","title":"A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images","date":"2018-03-07","arxiv_id":"1803.02786","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-expression-analysis-of-dynamical","title":"Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain","date":"2018-03-07","arxiv_id":"1803.02527","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-results-on-multi-step-traffic-flow","title":"New Results on Multi-Step Traffic Flow Prediction","date":"2018-03-04","arxiv_id":"1803.01365","repositories_listed":0,"syntology":null},{"url":null,"slug":"gan-based-synthetic-medical-image","title":"GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification","date":"2018-03-03","arxiv_id":"1803.01229","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-low-resource-machine-translation","title":"Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)","date":"2018-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ohiostate-at-semeval-2018-task-7-exploiting","title":"OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks","date":"2018-02-25","arxiv_id":"1802.08949","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-for-coniferdeciduous","title":"Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees","date":"2018-02-24","arxiv_id":"1802.08872","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensitivity-and-generalization-in-neural","title":"Sensitivity and Generalization in Neural Networks: an Empirical Study","date":"2018-02-23","arxiv_id":"1802.08760","repositories_listed":0,"syntology":null},{"url":"/paper/improved-techniques-for-weakly-supervised","slug":"improved-techniques-for-weakly-supervised","title":"Improved Techniques For Weakly-Supervised Object Localization","date":"2018-02-22","arxiv_id":"1802.07888","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-adversarial-synthesis-of-3d","title":"Conditional Adversarial Synthesis of 3D Facial Action Units","date":"2018-02-21","arxiv_id":"1802.07421","repositories_listed":0,"syntology":null},{"url":null,"slug":"da-gan-instance-level-image-translation-by","title":"DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)","date":"2018-02-18","arxiv_id":"1802.06454","repositories_listed":0,"syntology":null},{"url":"/paper/cnnlstm-architecture-for-speech-emotion","slug":"cnnlstm-architecture-for-speech-emotion","title":"CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation","date":"2018-02-15","arxiv_id":"1802.05630","repositories_listed":0,"syntology":null}],"record_sha256":"f17eb0ae4bab0a5257f6bdbb58d26e57847e2e969874dac5a59b67f40bc58804","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}