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 6451–6500 of 8378 papers

TitleStatusHype
MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classification—0
Mixup of Feature Maps in a Hidden Layer for Training of Convolutional Neural Network—0
Why Mixup Improves the Model Performance—0
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture—0
Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks—0
MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection—0
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling—0
MobileDepth: Efficient Monocular Depth Prediction on Mobile Devices—0
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild—0
Modality-Agnostic Debiasing for Single Domain Generalization—0
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection—0
MODALS: Data augmentation that works for everyone—0
MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis—0
Model Averaging and Augmented Inference for Stable Echocardiography Segmentation using 2D ConvNets—0
Model-Based Closed-Loop Control Algorithm for Stochastic Partial Differential Equation Control—0
Model-based Counterfactual Generator for Gender Bias Mitigation—0
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
Model-based Neural Data Augmentation for sub-wavelength Radio Localization—0
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation—0
Model-based Trajectory Stitching for Improved Offline Reinforcement Learning—0
Model compression using knowledge distillation with integrated gradients—0
Model Debiasing by Learnable Data Augmentation—0
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin—0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation—0
Models Out of Line: A Fourier Lens on Distribution Shift Robustness—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Momentum as Variance-Reduced Stochastic Gradient—0
Monitoring War Destruction from Space: A Machine Learning Approach—0
Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study—0
MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection—0
MoRe-Fi: Motion-robust and Fine-grained Respiration Monitoring via Deep-Learning UWB Radar—0
MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias—0
Morphological Signature for Improvement of Weakly Supervised Segmentation of Quadriceps Muscles on Magnetic Resonance Imaging Data—0
Motion Artifacts Detection in Short-scan Dental CBCT Reconstructions—0
Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform—0
MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks—0
MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease—0
MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup—0
MSM-VC: High-fidelity Source Style Transfer for Non-Parallel Voice Conversion by Multi-scale Style Modeling—0
MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion—0
MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation—0
MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis—0
MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER—0
MULE: Multimodal Universal Language Embedding—0
Multi-Agent Automated Machine Learning—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery—0
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy—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