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 6476–6500 of 8378 papers

TitleStatusHype
Data Augmentation for Improving the Prediction of Validity and Novelty of Argumentative Conclusions—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
Data Augmentation for Intent Classification—0
Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain—0
Data Augmentation For Label Enhancement—0
Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants—0
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings—0
Data Augmentation for Low-Resource Dialogue Summarization—0
Data Augmentation for Low-Resource Dialogue Summarization—0
Data augmentation for low-resource grapheme-to-phoneme mapping—0
Data Augmentation for Low-Resource Quechua ASR Improvement—0
Data augmentation for low resource sentiment analysis using generative adversarial networks—0
Data Augmentation for Low-resource Word Segmentation and POS Tagging of Ancient Chinese Texts—0
Data augmentation for machine learning of chemical process flowsheets—0
Data Augmentation for Mathematical Objects—0
Data Augmentation For Medical MR Image Using Generative Adversarial Networks—0
Data Augmentation for Mental Health Classification on Social Media—0
Data Augmentation for Modeling Human Personality: The Dexter Machine—0
Data Augmentation for Morphological Reinflection—0
Data Augmentation for Multiclass Utterance Classification -- A Systematic Study—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Data Augmentation for NeRFs in the Low Data Limit—0
Data Augmentation for Neural Machine Translation using Generative Language Model—0
Data Augmentation for Neural NLP—0
Data Augmentation for Neural Online Chat Response Selection—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