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:

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5851–5875 of 8378 papers

TitleStatusHype
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture—0
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT—0
Automated Classification of Cybercrime Complaints using Transformer-based Language Models for Hinglish Texts—0
Automated Contrastive Learning Strategy Search for Time Series—0
Automated Data Augmentations for Graph Classification—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Automated detection of Alzheimer disease using MRI images and deep neural networks- A review—0
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks—0
Automated Detection of hidden Damages and Impurities in Aluminum Die Casting Materials and Fibre-Metal Laminates using Low-quality X-ray Radiography, Synthetic X-ray Data Augmentation by Simulation, and Machine Learning—0
Automated ensemble method for pediatric brain tumor segmentation—0
Data Augmentation for Automated Essay Scoring using Transformer Models—0
Comparison of algorithms in Foreign Exchange Rate Prediction—0
Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network—0
Automated segmentation of microvessels in intravascular OCT images using deep learning—0
Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning—0
AutoMathKG: The automated mathematical knowledge graph based on LLM and vector database—0
Automatic Airway Segmentation in chest CT using Convolutional Neural Networks—0
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets—0
Automatic brain tissue segmentation in fetal MRI using convolutional neural networks—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets—0
Automatic Cerebral Vessel Extraction in TOF-MRA Using Deep Learning—0
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning—0
Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders—0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations—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