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 7001–7050 of 8378 papers

TitleStatusHype
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View SynthesisCode0
A Deep Convolutional Neural Network for the Detection of Polyps in Colonoscopy Images—0
Optimized Deep Encoder-Decoder Methods for Crack Segmentation—0
Adaptation Algorithms for Neural Network-Based Speech Recognition: An OverviewCode0
Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data—0
Mask Detection and Breath Monitoring from Speech: on Data Augmentation, Feature Representation and Modeling—0
Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation—0
Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams—0
Surgical Mask Detection with Convolutional Neural Networks and Data Augmentations on Spectrograms—0
Transformer with Bidirectional Decoder for Speech Recognition—0
PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks—0
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
On the Accuracy of CRNNs for Line-Based OCR: A Multi-Parameter Evaluation—0
Retrieve Synonymous keywords for Frequent Queries in Sponsored Search in a Data Augmentation Way—0
Autoencoder Image Interpolation by Shaping the Latent Space—0
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning—0
Spherical Feature Transform for Deep Metric Learning—0
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization—0
Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech—0
Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery—0
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation—0
Removing Backdoor-Based Watermarks in Neural Networks with Limited Data—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation—0
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning—0
LungRN+NL: An Improved Adventitious Lung Sound Classification Using Non-Local Block ResNet Neural Network with Mixup Data Augmentation—0
Adversarial Data Augmentation via Deformation Statistics—0
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation—0
Learning Object Placement by Inpainting for Compositional Data Augmentation—0
Rethinking the Defocus Blur Detection Problem and A Real-Time Deep DBD Model—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler—0
Paying Per-label Attention for Multi-label Extraction from Radiology Reports—0
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances—0
Robust Retinal Vessel Segmentation from a Data Augmentation Perspective—0
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks—0
Learning from Few Samples: A Survey—0
Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
Representation Learning with Video Deep InfoMax—0
Semi-Supervised Learning with Data Augmentation for End-to-End ASR—0
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing—0
Normal-bundle BootstrapCode0
Self-supervised Learning for Large-scale Item Recommendations—0
Counting Fish and Dolphins in Sonar Images Using Deep Learning—0
SeismoFlow -- Data augmentation for the class imbalance problem—0
How Does Data Augmentation Affect Privacy in Machine Learning?Code0
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation—0
Investigating Bias and Fairness in Facial Expression Recognition—0
On regularization of gradient descent, layer imbalance and flat minima—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