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

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Showing 4551–4600 of 8378 papers

TitleStatusHype
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering—0
Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image GeneratorsCode0
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data—0
On the Applicability of Synthetic Data for Re-IdentificationCode0
On-the-fly Denoising for Data Augmentation in Natural Language UnderstandingCode0
Rumour detection using graph neural network and oversampling in benchmark Twitter dataset—0
VoronoiPatches: Evaluating A New Data Augmentation Method—0
Emotion Selectable End-to-End Text-based Speech Editing—0
Original or Translated? On the Use of Parallel Data for Translation Quality Estimation—0
Visual Transformers for Primates Classification and Covid Detection—0
An Augmentation Strategy for Visually Rich Documents—0
End to End Generative Meta Curriculum Learning For Medical Data Augmentation—0
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models—0
A Twitter BERT Approach for Offensive Language Detection in Marathi—0
Flareon: Stealthy any2any Backdoor Injection via Poisoned AugmentationCode0
Synthetic Data Augmentation Using GAN For Improved Automated Visual Inspection—0
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based AugmentationsCode0
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning—0
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer—0
Predicting Ejection Fraction from Chest X-rays Using Computer Vision for Diagnosing Heart Failure—0
Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?—0
PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment—0
AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data AugmentationCode0
Human Image Generation: A Comprehensive Survey—0
Balanced Split: A new train-test data splitting strategy for imbalanced datasetsCode0
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image ClassificationCode0
Check-worthy Claim Detection across Topics for Automated Fact-checking—0
Multi-Scales Data Augmentation Approach In Natural Language Inference For Artifacts Mitigation And Pre-Trained Model Optimization—0
ColorSense: A Study on Color Vision in Machine Visual Recognition—0
Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation—0
The Effects of Character-Level Data Augmentation on Style-Based Dating of Historical ManuscriptsCode0
SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image—0
The effects of gender bias in word embeddings on depression prediction—0
DeepJoin: Joinable Table Discovery with Pre-trained Language Models—0
Multi-VALUE: A Framework for Cross-Dialectal English NLP—0
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications—0
Generative Robust Classification—0
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation—0
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture—0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification—0
Robust Policy Optimization in Deep Reinforcement LearningCode0
Improving Depression estimation from facial videos with face alignment, training optimization and scheduling—0
Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis—0
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps—0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Zero-Shot Accent Conversion using Pseudo Siamese Disentanglement Network—0
RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language UnderstandingCode0
Cap2Aug: Caption guided Image to Image data Augmentation—0
Teaching What You Should Teach: A Data-Based Distillation Method—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