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 4601–4650 of 8378 papers

TitleStatusHype
Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery—0
RandoMix: A mixed sample data augmentation method with multiple mixed modes—0
Random Padding Data Augmentation—0
Random Smoothing Regularization in Kernel Gradient Descent Learning—0
Random Walks in Self-supervised Learning for Triangular Meshes—0
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion—0
Rapid analysis of point-contact Andreev reflection spectra via machine learning with adaptive data augmentation—0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments—0
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks—0
RCRNN-based Sound Event Detection System with Specific Speech Resolution—0
Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese—0
Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection—0
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning—0
Realistic Hair Simulation Using Image Blending—0
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks—0
Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness—0
Real-Time Facial Segmentation and Performance Capture from RGB Input—0
Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning—0
Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion—0
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning—0
Real-Time Radiance Fields for Single-Image Portrait View Synthesis—0
Real-time Streaming Perception System for Autonomous Driving—0
Real-Time Well Log Prediction From Drilling Data Using Deep Learning—0
Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming—0
Real-world plant species identification based on deep convolutional neural networks and visual attention—0
Reappraising Domain Generalization in Neural Networks—0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao—0
Recency Dropout for Recurrent Recommender Systems—0
Recent Advances in Direct Speech-to-text Translation—0
Recent Advances of Generic Object Detection with Deep Learning: A Review—0
Recent Progress in the CUHK Dysarthric Speech Recognition System—0
Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction—0
Recognising Biomedical Names: Challenges and Solutions—0
Recognition and Synthesis of Object Transport Motion—0
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniques—0
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective—0
Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding—0
Reconstruct from BEV: A 3D Lane Detection Approach based on Geometry Structure Prior—0
Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior—0
Reconstructing A Large Scale 3D Face Dataset for Deep 3D Face Identification—0
Reconstructing neuronal anatomy from whole-brain images—0
Recover from Horcrux: A Spectrogram Augmentation Method for Cardiac Feature Monitoring from Radar Signal Components—0
Recovering and Simulating Pedestrians in the Wild—0
Recurrent Coupled Topic Modeling over Sequential Documents—0
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems—0
Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks—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