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 4301–4350 of 8378 papers

TitleStatusHype
Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing—0
Simulation-Enhanced Data Augmentation for Machine Learning Pathloss Prediction—0
Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects—0
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning—0
SimVQA: Exploring Simulated Environments for Visual Question Answering—0
SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy—0
Single Domain Generalization via Normalised Cross-correlation Based Convolutions—0
Single Domain Generalization with Model-aware Parametric Batch-wise Mixup—0
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard—0
Single-image driven 3d viewpoint training data augmentation for effective wine label recognition—0
Single Person Pose Estimation: A Survey—0
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers—0
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation—0
Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation—0
SIRfyN: Single Image Relighting from your Neighbors—0
SIRL: Similarity-based Implicit Representation Learning—0
Skeleton and Font Generation Network for Zero-shot Chinese Character Generation—0
Sketch Me That Shoe—0
SketchODE: Learning neural sketch representation in continuous time—0
Sketch-Specific Data Augmentation for Freehand Sketch Recognition—0
Skin Cancer Images Classification using Transfer Learning Techniques—0
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning—0
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning—0
SkoltechNLP at SemEval-2020 Task 11: Exploring Unsupervised Text Augmentation for Propaganda Detection—0
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis—0
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization—0
SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages—0
SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms—0
Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies—0
Small data deep learning methodology for in-field disease detection—0
Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation—0
Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Small Target Detection for Search and Rescue Operations using Distributed Deep Learning and Synthetic Data Generation—0
Smart Augmentation - Learning an Optimal Data Augmentation Strategy—0
Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation—0
SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing—0
S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search—0
SmoothMix: A Simple Yet Effective Data Augmentation to Train Robust Classifiers—0
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation—0
SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation—0
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data—0
Soccer jersey number recognition using convolutional neural networks—0
Social Media Bot Detection using Dropout-GAN—0
Robust Training of Social Media Image Classification Models for Rapid Disaster Response—0
SODA: Self-organizing data augmentation in deep neural networks -- Application to biomedical image segmentation tasks—0
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping—0
Soft-CP: A Credible and Effective Data Augmentation for Semantic Segmentation of Medical Lesions—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