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.

Further readings:

( Image credit: Albumentations )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3501–3550 of 8378 papers

TitleStatusHype
A Kernel Theory of Modern Data Augmentation—0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference—0
CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction—0
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations—0
Coordination Generation via Synchronized Text-Infilling—0
A Transformer Based Pitch Sequence Autoencoder with MIDI Augmentation—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
Domain specific cues improve robustness of deep learning based segmentation of ct volumes—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection—0
Adaptive Unbiased Teacher for Cross-Domain Object Detection—0
Accelerated Neural Network Training with Rooted Logistic Objectives—0
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge—0
Dialog State Tracking with Reinforced Data Augmentation—0
Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping—0
Convolutional Neural Networks for Font Classification—0
Convolutional Neural Networks for Automatic Meter Reading—0
A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset—0
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation—0
A Theory of PAC Learnability under Transformation Invariances—0
A Target-Aware Analysis of Data Augmentation for Hate Speech Detection—0
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling—0
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image—0
A tailored Handwritten-Text-Recognition System for Medieval Latin—0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents—0
A Systematic Study on Quantifying Bias in GAN-Augmented Data—0
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning—0
Adaptive Regularization of Labels—0
A Causal View on Robustness of Neural Networks—0
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting—0
Controllable Top-down Feature Transformer—0
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks—0
Controllable Text Simplification with Explicit Paraphrasing—0
FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space—0
GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU—0
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy—0
DeepC2: AI-powered Covert Command and Control on OSNs—0
GCC: Generative Color Constancy via Diffusing a Color Checker—0
Show:102550
← PrevPage 71 of 168Next →
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