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 4651–4675 of 8378 papers

TitleStatusHype
Redefining Self-Normalization Property—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
Reducing false positives in strong lens detection through effective augmentation and ensemble learning—0
Reducing Gender Bias in Abusive Language Detection—0
Reducing Overfitting in Deep Networks by Decorrelating Representations—0
REFINE on Scarce Data: Retrieval Enhancement through Fine-Tuning via Model Fusion of Embedding Models—0
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction—0
ReFormer: Generating Radio Fakes for Data Augmentation—0
ReF -- Rotation Equivariant Features for Local Feature Matching—0
Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph—0
Region Mixup—0
Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images—0
Regularising Deep Networks with Deep Generative Models—0
Regularising for invariance to data augmentation improves supervised learning—0
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling—0
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning—0
Regularizing Contrastive Predictive Coding for Speech Applications—0
Regularizing Neural Networks with Meta-Learning Generative Models—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation—0
Relate auditory speech to EEG by shallow-deep attention-based network—0
Relational Data Selection for Data Augmentation of Speaker-dependent Multi-band MelGAN Vocoder—0
Relation-Aware Graph Foundation Model—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