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 2801–2825 of 8378 papers

TitleStatusHype
A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification—0
Domain Generalization -- A Causal Perspective—0
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)—0
Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization—0
Evolving Image Compositions for Feature Representation Learning—0
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue—0
Effective Data Augmentation for Sentence Classification Using One VAE per Class—0
Domain Gap Embeddings for Generative Dataset Augmentation—0
Effective Data Augmentation with Multi-Domain Learning GANs—0
Can segmentation models be trained with fully synthetically generated data?—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Effective face landmark localization via single deep network—0
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology—0
Effective LLM Knowledge Learning via Model Generalization—0
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation—0
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data—0
Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer—0
Can Synthetic Translations Improve Bitext Quality?—0
Effect of GAN augmented dataset size on deep learning-based ultrasound bone segmentation model training—0
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning—0
Can the accuracy bias by facial hairstyle be reduced through balancing the training data?—0
Effects of Using Synthetic Data on Deep Recommender Models' Performance—0
Domain-Agnostic Clustering with Self-Distillation—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—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