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.

Further readings:

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7026–7050 of 8378 papers

TitleStatusHype
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 2×96d) (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