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 6951–7000 of 8378 papers

TitleStatusHype
NEU at WNUT-2020 Task 2: Data Augmentation To Tell BERT That Death Is Not Necessarily Informative—0
DeepC2: AI-powered Covert Command and Control on OSNs—0
Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features—0
Data Augmentation and Clustering for Vehicle Make/Model Classification—0
Contrastive Self-supervised Learning for Graph Classification—0
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field—0
Fair and accurate age prediction using distribution aware data curation and augmentationCode0
PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans—0
On Target Segmentation for Direct Speech Translation—0
Accelerating Real-Time Question Answering via Question Generation—0
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective—0
Revealing Lung Affections from CTs. A Comparative Analysis of Various Deep Learning Approaches for Dealing with Volumetric Data—0
Intraoperative Liver Surface Completion with Graph Convolutional VAE—0
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge GraphsCode0
The 2ST-UNet for Pneumothorax Segmentation in Chest X-Rays using ResNet34 as a Backbone for U-Net—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
Data Augmentation for Electrocardiogram Classification with Deep Neural Network—0
A Practical Layer-Parallel Training Algorithm for Residual Networks—0
Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation—0
MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance—0
A general approach to bridge the reality-gap—0
Breast mass detection in digital mammography based on anchor-free architecture—0
Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images—0
Robust Object Classification Approach using Spherical Harmonics—0
Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge DistillationCode0
To augment or not to augment? Data augmentation in user identification based on motion sensors—0
Quality-aware semi-supervised learning for CMR segmentation—0
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions—0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images—0
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization—0
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset—0
Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN—0
Defect Prediction of Railway Wheel Flats based on Hilbert Transform and Wavelet Packet Decomposition—0
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition—0
Synthetic Sample Selection via Reinforcement Learning—0
m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural NetworksCode0
Data augmentation techniques for the Video Question Answering task—0
Self-Competitive Neural Networks—0
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory—0
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles—0
On Nondeterminism and Instability in Optimizing Neural Networks—0
A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays—0
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training—0
Robust Handwriting Recognition with Limited and Noisy Data—0
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences—0
BUT-FIT at SemEval-2020 Task 4: Multilingual commonsenseCode0
Cross-Modality 3D Object Detection—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