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

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Showing 3701–3725 of 8378 papers

TitleStatusHype
Gibbs Max-margin Topic Models with Data Augmentation—0
GIMM: InfoMin-Max for Automated Graph Contrastive Learning—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Global and Local Information Based Deep Network for Skin Lesion Segmentation—0
Global Context Is All You Need for Parallel Efficient Tractography Parcellation—0
Contrastive Learning with Negative Sampling Correction—0
A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Global Mixup: Eliminating Ambiguity with Clustering Relationships—0
Global Mixup: Eliminating Ambiguity with Clustering—0
Functional Space Analysis of Local GAN Convergence—0
Fully Test-time Adaptation for Tabular Data—0
A Survey on Neural Architecture Search—0
Fully Bayesian inference for neural models with negative-binomial spiking—0
Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images—0
Fully Automatic Electrocardiogram Classification System based on Generative Adversarial Network with Auxiliary Classifier—0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Goal-Embedded Dual Hierarchical Model for Task-Oriented Dialogue Generation—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives—0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks—0
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences—0
Full-Frame Scene Coordinate Regression for Image-Based Localization—0
Contrastive Learning is Just Meta-Learning—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