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 7251–7300 of 8378 papers

TitleStatusHype
Physically Realizable Adversarial Examples for LiDAR Object Detection—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation—0
Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract—0
Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
Gradient-based Data Augmentation for Semi-Supervised Learning—0
A Close Look at Deep Learning with Small Data—0
Fashion Landmark Detection and Category Classification for RoboticsCode0
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation—0
Fast Cross-domain Data Augmentation through Neural Sentence Editing—0
On Calibration of Mixup Training for Deep Neural NetworksCode0
ARDA: Automatic Relational Data Augmentation for Machine LearningCode0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks—0
Local Rotation Invariance in 3D CNNsCode0
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images—0
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation—0
Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating—0
Revisit Systematic Generalization via Meaningful LearningCode0
On the effectiveness of convolutional autoencoders on image-based personalized recommender systems—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection—0
Stateful Premise Selection by Recurrent Neural Networks—0
Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
DeepSperm: A robust and real-time bull sperm-cell detection in densely populated semen videos—0
XGPT: Cross-modal Generative Pre-Training for Image Captioning—0
Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images—0
Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation—0
Do CNNs Encode Data Augmentations?—0
Unshuffling Data for Improved Generalization—0
Imbalance Learning for Variable Star ClassificationCode0
Time Series Data Augmentation for Deep Learning: A Survey—0
SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech TranslationCode0
Data Augmentation for Personal Knowledge Base Population—0
Data Augmentation for Copy-Mechanism in Dialogue State Tracking—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Training Question Answering Models From Synthetic Data—0
A Multi-view Perspective of Self-supervised Learning—0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation—0
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation—0
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition—0
Wavesplit: End-to-End Speech Separation by Speaker Clustering—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos—0
Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi—0
Undersensitivity in Neural Reading Comprehension—0
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data—0
CEB Improves Model RobustnessCode0
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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