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 6126–6150 of 8378 papers

TitleStatusHype
Learning for Counterfactual Fairness from Observational Data—0
Learning from Data-Rich Problems: A Case Study on Genetic Variant Calling—0
Learning from Few Samples: A Survey—0
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding—0
Learning from One Continuous Video Stream—0
Learning Gaussian Data Augmentation in Feature Space for One-shot Object Detection in Manga—0
Learning Generalizable Models via Disentangling Spurious and Enhancing Potential Correlations—0
Learning Human Perception Dynamics for Informative Robot Communication—0
Learning Instance-Specific Adaptation for Cross-Domain Segmentation—0
Learning Invariances using the Marginal Likelihood—0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Learning linear modules in a dynamic network with missing node observations—0
Learning Linear Symmetries in Data Using Moment Matching—0
Learning Loss for Test-Time Augmentation—0
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images—0
Learning More with Less: GAN-based Medical Image Augmentation—0
Learning Neural Light Transport—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD—0
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM—0
Learning Object Placement by Inpainting for Compositional Data Augmentation—0
Learning Online Visual Invariances for Novel Objects via Supervised and Self-Supervised Training—0
Learning Physical Common Sense as Knowledge Graph Completion via BERT Data Augmentation and Constrained Tucker Factorization—0
Learning Polynomial Problems with SL(2,R) Equivariance—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