SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

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Papers

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Showing 4501–4550 of 8378 papers

TitleStatusHype
Predicting high dengue incidence in municipalities of Brazil using path signatures—0
Predicting mechanical properties of Carbon Nanotube (CNT) images Using Multi-Layer Synthetic Finite Element Model Simulations—0
Predicting Out-of-Domain Generalization with Neighborhood Invariance—0
Predicting Poverty Level from Satellite Imagery using Deep Neural Networks—0
Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning—0
CNN-MoE based framework for classification of respiratory anomalies and lung disease detection—0
Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation—0
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting—0
Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification—0
Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues—0
Prediction of Diblock Copolymer Morphology via Machine Learning—0
Prediction of the Facial Growth Direction is Challenging—0
Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models—0
PressureTransferNet: Human Attribute Guided Dynamic Ground Pressure Profile Transfer using 3D simulated Pressure Maps—0
Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text—0
Pre-Training for 3D Hand Pose Estimation with Contrastive Learning on Large-Scale Hand Images in the Wild—0
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection—0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
Principled Ultrasound Data Augmentation for Classification of Standard Planes—0
PriorityCut: Occlusion-aware Regularization for Image Animation—0
Automatic Financial Feature Construction—0
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting—0
Privacy-Preserving Debiasing using Data Augmentation and Machine Unlearning—0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition—0
Probing the Information Encoded in X-vectors—0
Procurements with Bidder Asymmetry in Cost and Risk-Aversion—0
Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection—0
Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices—0
Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices—0
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation—0
ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition—0
Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data—0
Prompt-based System for Personality and Interpersonal Reactivity Prediction—0
Prompt-guided Scene Generation for 3D Zero-Shot Learning—0
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks—0
Prompt Perturbation Consistency Learning for Robust Language Models—0
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases—0
Properties of the After Kernel—0
Proposing an intelligent mesh smoothing method with graph neural networks—0
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI—0
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks—0
Prostate Gland Segmentation in Histology Images via Residual and Multi-Resolution U-Net—0
Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
Provable Benefit of Cutout and CutMix for Feature Learning—0
Provable Benefit of Mixup for Finding Optimal Decision Boundaries—0
ProxiMix: Enhancing Fairness with Proximity Samples in Subgroups—0
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning—0
PS4PRO: Pixel-to-pixel Supervision for Photorealistic Rendering and Optimization—0
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified