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 3651–3675 of 8378 papers

TitleStatusHype
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Generative Data Augmentation for Vehicle Detection in Aerial Images—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
Human Age Estimation from Gene Expression Data using Artificial Neural Networks—0
Boosting Model Resilience via Implicit Adversarial Data Augmentation—0
Boosting Masked Face Recognition with Multi-Task ArcFace—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
Generatively Augmented Neural Network Watchdog for Image Classification Networks—0
Direct Coloring for Self-Supervised Enhanced Feature Decoupling—0
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—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
Generative Models for Multi-Illumination Color Constancy—0
Generative Networks for Precision Enthusiasts—0
Generative Retrieval for Book search—0
Generative Robust Classification—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling—0
Generative Technology for Human Emotion Recognition: A Scope Review—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
HpEIS: Learning Hand Pose Embeddings for Multimedia Interactive Systems—0
Human Pose Transfer with Augmented Disentangled Feature Consistency—0
Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation—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