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 5101–5150 of 8378 papers

TitleStatusHype
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
SoftEdge: Regularizing Graph Classification with Random Soft Edges—0
Softplus Regressions and Convex Polytopes—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
SoilingNet: Soiling Detection on Automotive Surround-View Cameras—0
SOLA-GCL: Subgraph-Oriented Learnable Augmentation Method for Graph Contrastive Learning—0
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL—0
SolomonLab at SemEval-2019 Task 8: Question Factuality and Answer Veracity Prediction in Community Forums—0
Solution for CVPR 2024 UG2+ Challenge Track on All Weather Semantic Segmentation—0
SOMson -- Sonification of Multidimensional Data in Kohonen Maps—0
Sorted Convolutional Network for Achieving Continuous Rotational Invariance—0
SOS: A Shuffle Order Strategy for Data Augmentation in Industrial Human Activity Recognition—0
Toward Student-Oriented Teacher Network Training For Knowledge Distillation—0
Sound Classification of Four Insect Classes—0
Sound Event Detection in Domestic Environments using Dense Recurrent Neural Network—0
Sound Tagging in Infant-centric Home Soundscapes—0
Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems—0
SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking—0
SpanAlign: Efficient Sequence Tagging Annotation Projection into Translated Data applied to Cross-Lingual Opinion Mining—0
SpanDrop: Simple and Effective Counterfactual Learning for Long Sequences—0
Sparse annotation strategies for segmentation of short axis cardiac MRI—0
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations—0
Spatially Exclusive Pasting: A General Data Augmentation for the Polyp Segmentation—0
Spatial Reasoning for Few-Shot Object Detection—0
Spatial-temporal Transformer for Affective Behavior Analysis—0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition—0
Resisting Large Data Variations via Introspective Transformation Network—0
Spatio-Temporal Attention Pooling for Audio Scene Classification—0
Frequency Selective Augmentation for Video Representation Learning—0
Spatio-temporal Data Augmentation for Visual Surveillance—0
Spatiotemporal deep learning models for detection of rapid intensification in cyclones—0
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation—0
Spatiotemporal Learning of Dynamic Gestures from 3D Point Cloud Data—0
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions—0
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation—0
SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-M—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified