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:

( Image credit: Albumentations )

Papers

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Showing 3626–3650 of 8378 papers

TitleStatusHype
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU—0
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Generative Adversarial Networks for Data Augmentation—0
Generative adversarial networks for data-scarce spectral applications—0
Boosting Neural Machine Translation with Similar Translations—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans—0
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems—0
Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks—0
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification—0
Generative AI for Physical-Layer Authentication—0
Boosting Model Resilience via Implicit Adversarial Data Augmentation—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Boosting Masked Face Recognition with Multi-Task ArcFace—0
Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations?—0
Generative Cooperative Net for Image Generation and Data Augmentation—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning—0
Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation—0
Digital Signal Processing Using Deep Neural Networks—0
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation—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