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

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Showing 5601–5650 of 8378 papers

TitleStatusHype
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset—0
A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education—0
A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions—0
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification—0
A Novel Dataset for Financial Education Text Simplification in Spanish—0
Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities—0
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection—0
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation—0
A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)—0
A novel method to enhance pneumonia detection via a model-level ensembling of CNN and vision transformer—0
A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
A novel network training approach for open set image recognition—0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification—0
An overview of mixing augmentation methods and augmentation strategies—0
Anticipating the Unseen Discrepancy for Vision and Language Navigation—0
Anti-Confusing: Region-Aware Network for Human Pose Estimation—0
Anti-Inpainting: A Proactive Defense against Malicious Diffusion-based Inpainters under Unknown Conditions—0
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images—0
An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea—0
An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio—0
An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions—0
ANVITA Machine Translation System for WAT 2021 MultiIndicMT Shared Task—0
Anything in Any Scene: Photorealistic Video Object Insertion—0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
APAC: Augmented PAttern Classification with Neural Networks—0
A Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation—0
A Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification—0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework—0
A Picture May Be Worth a Hundred Words for Visual Question Answering—0
A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation—0
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning—0
Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks—0
Application of Deep Learning Methods to SNOMED CT Encoding of Clinical Texts: From Data Collection to Extreme Multi-Label Text-Based Classification—0
Application of Mix-Up Method in Document Classification Task Using BERT—0
Application of multilayer perceptron with data augmentation in nuclear physics—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks—0
A Preliminary Study on Data Augmentation of Deep Learning for Image Classification—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation—0
A Pre-trained Data Deduplication Model based on Active Learning—0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
A proximal policy optimization based intelligent home solar management—0
APT: Adaptive Personalized Training for Diffusion Models with Limited Data—0
A PubMedBERT-based Classifier with Data Augmentation Strategy for Detecting Medication Mentions in Tweets—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