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 5251–5300 of 8378 papers

TitleStatusHype
Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation—0
Balancing Data through Data Augmentation Improves the Generality of Transfer Learning for Diabetic Retinopathy Classification—0
EventMix: An Efficient Augmentation Strategy for Event-Based Data—0
Multi-Augmentation for Efficient Visual Representation Learning for Self-supervised Pre-trainingCode0
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint—0
Robust 3D Object Detection in Cold Weather Conditions—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Data augmentation for efficient learning from parametric experts—0
Learning to Ignore Adversarial Attacks—0
Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust ModelsCode0
Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies—0
CNNs Avoid Curse of Dimensionality by Learning on Patches—0
Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation—0
Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection—0
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration—0
Swapping Semantic Contents for Mixing Images—0
Data Augmentation for Compositional Data: Advancing Predictive Models of the MicrobiomeCode0
Combining Contrastive and Supervised Learning for Video Super-Resolution DetectionCode0
Semi-self-supervised Automated ICD Coding—0
MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion—0
The AI Mechanic: Acoustic Vehicle Characterization Neural Networks—0
VNT-Net: Rotational Invariant Vector Neuron Transformers—0
Transformers as Neural Augmentors: Class Conditional Sentence Generation via Variational BayesCode0
Cross-lingual Inflection as a Data Augmentation Method for Parsing—0
Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial NetworksCode0
Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic SegmentationCode0
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation—0
SemiCurv: Semi-Supervised Curvilinear Structure SegmentationCode0
Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots—0
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot LearnersCode0
RandoMix: A mixed sample data augmentation method with multiple mixed modes—0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation—0
Empirical Advocacy of Bio-inspired Models for Robust Image RecognitionCode0
PreQuEL: Quality Estimation of Machine Translation Outputs in AdvanceCode0
Application of multilayer perceptron with data augmentation in nuclear physics—0
A Survey on Semantics in Automated Data Science—0
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review—0
Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing—0
Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learning—0
Corrosion Detection for Industrial Objects: From Multi-Sensor System to 5D Feature Space—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition—0
Zero-shot Code-Mixed Offensive Span Identification through Rationale ExtractionCode0
Simple Contrastive Graph Clustering—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
White-box Testing of NLP models with Mask Neuron Coverage—0
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation—0
Show:102550
← PrevPage 106 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