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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6751–6800 of 8378 papers

TitleStatusHype
Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data—0
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation—0
TinaFace: Strong but Simple Baseline for Face DetectionCode0
A Unified Mixture-View Framework for Unsupervised Representation Learning—0
StackMix: A complementary Mix algorithm—0
Improving Augmentation and Evaluation Schemes for Semantic Image Synthesis—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—0
Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech—0
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks—0
Transfer Learning for Oral Cancer Detection using Microscopic Images—0
Cancer image classification based on DenseNet model—0
Learnable Gabor modulated complex-valued networks for orientation robustness—0
MobileDepth: Efficient Monocular Depth Prediction on Mobile Devices—0
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning—0
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment—0
Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Visual Diver Face Recognition for Underwater Human-Robot Interaction—0
Self-supervised Document Clustering Based on BERT with Data Augment—0
Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization—0
Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection—0
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?—0
Facebook AI's WMT20 News Translation Task Submission—0
Recovering and Simulating Pedestrians in the Wild—0
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge BasesCode0
Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI—0
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images—0
Unsupervised Learning of Dense Visual Representations—0
Low-resource expressive text-to-speech using data augmentation—0
Medical Knowledge-enriched Textual Entailment Framework—0
UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-task Learning on Self-Supervised AnnotationsCode0
Text Classification through Glyph-aware Disentangled Character Embedding and Semantic Sub-character AugmentationCode0
LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning—0
An improved helmet detection method for YOLOv3 on an unbalanced dataset—0
Towards Domain-Agnostic Contrastive Learning—0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Teaching with CommentariesCode0
Data Augmentation via Structured Adversarial Perturbations—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
Investigating Societal Biases in a Poetry Composition SystemCode0
End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales—0
Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest—0
Data Augmentation and Terminology Integration for Domain-Specific Sinhala-English-Tamil Statistical Machine Translation—0
Sound Event Detection in Domestic Environments using Dense Recurrent Neural Network—0
Deep Multi-task Network for Delay Estimation and Echo Cancellation—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Learning Regional Purity for Instance Segmentation on 3D Point CloudsCode0
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks—0
Training Wake Word Detection with Synthesized Speech Data on Confusion Words—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