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 3226–3250 of 8378 papers

TitleStatusHype
Amsqr at SemEval-2022 Task 4: Towards AutoNLP via Meta-Learning and Adversarial Data Augmentation for PCL Detection—0
Data Augmentation for Deep Learning-based Radio Modulation Classification—0
Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models—0
Data augmentation for dealing with low sampling rates in NILM—0
Data Augmentation for Copy-Mechanism in Dialogue State Tracking—0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Accurate Face Detection for High Performance—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs—0
AugmentTRAJ: A framework for point-based trajectory data augmentation—0
Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data—0
Data Augmentation for Biomedical Factoid Question Answering—0
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages—0
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models—0
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering—0
Data Augmentation for Automated Adaptive Rodent Training—0
Data Augmentation Can Improve Robustness—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Data Augmentation by Pairing Samples for Images Classification—0
Augment on Manifold: Mixup Regularization with UMAP—0
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding—0
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—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