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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5551–5575 of 8378 papers

TitleStatusHype
An Efficient and Small Convolutional Neural Network for Pest Recognition -- ExquisiteNet—0
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation—0
Efficient Training of Generalizable Visuomotor Policies via Control-Aware Augmentation—0
An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training—0
An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification—0
An Empathetic User-Centric Chatbot for Emotional Support—0
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation—0
An Empirical Comparison of LM-based Question and Answer Generation Methods—0
An empirical investigation into audio pipeline approaches for classifying bird species—0
An Empirical Study of Aegis—0
An Empirical Study of Automatic Post-Editing—0
An Empirical Study of Causal Relation Extraction Transfer: Design and Data—0
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution—0
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation—0
An Empirical Study on Multi-Domain Robust Semantic Segmentation—0
An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting—0
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP—0
An Empirical Survey of Data Augmentation \ Limited Data Learning in NLP—0
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models—0
An Enhanced Prohibited Items Recognition Model—0
An Ensemble of Convolutional Neural Networks for Audio Classification—0
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient—0
An evaluation of data augmentation methods for sound scene geotagging—0
A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions—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