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.

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Papers

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Showing 7101–7150 of 8378 papers

TitleStatusHype
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation—0
PhishGAN: Data Augmentation and Identification of Homoglpyh Attacks—0
Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation—0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph LearningCode0
Probabilistic Structural Latent Representation for Unsupervised EmbeddingCode0
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation—0
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation—0
A general framework for defining and optimizing robustness—0
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift—0
Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs—0
Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement—0
DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationCode0
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior—0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data—0
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features—0
Meta Approach to Data Augmentation Optimization—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context—0
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification—0
Hypernetwork-Based Augmentation—0
Why Mixup Improves the Model Performance—0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
On Mixup RegularizationCode0
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation—0
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning—0
Unsupervised Paraphrase Generation using Pre-trained Language Models—0
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech—0
The Penalty Imposed by Ablated Data Augmentation—0
A Transductive Multi-Head Model for Cross-Domain Few-Shot LearningCode0
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation—0
Enhancing Facial Data Diversity with Style-based Face Aging—0
Learning Diagnosis of COVID-19 from a Single Radiological ImageCode0
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images—0
Learning Neural Light Transport—0
Handling missing data in model-based clustering—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models—0
Learning Augmentation Network via Influence Functions—0
Probabilistic Structural Latent Representation for Unsupervised EmbeddingCode0
A U-Net Based Discriminator for Generative Adversarial Networks—0
Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework—0
SmoothMix: A Simple Yet Effective Data Augmentation to Train Robust Classifiers—0
A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks—0
Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methodsCode0
Pseudo-Representation Labeling Semi-Supervised Learning—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
A Comparative Study of Lexical Substitution Approaches based on Neural Language Models—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