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 2801–2850 of 8378 papers

TitleStatusHype
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation—0
Domain generalization for retinal vessel segmentation via Hessian-based vector field—0
Adversarial Data Augmentation via Deformation Statistics—0
EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox—0
Domain Generalization for In-Orbit 6D Pose Estimation—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 Generalization Emerges from Dreaming—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
An overview of mixing augmentation methods and augmentation strategies—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
Bridging the visual gap in VLN via semantically richer instructions—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 Generalization: A Survey—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models—0
CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures—0
A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification—0
Efficient Augmentation via Data Subsampling—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
Efficient Classification of Histopathology Images—0
Explainable Deep Learning for Augmentation of sRNA Expression Profiles—0
Efficient data augmentation using graph imputation neural networks—0
Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging—0
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?—0
Explainable Deep Learning Framework for Human Activity Recognition—0
Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation—0
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training—0
Domain Gap Embeddings for Generative Dataset Augmentation—0
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer—0
Domain-Agnostic Clustering with Self-Distillation—0
Efficient, Lexicon-Free OCR using Deep Learning—0
Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization—0
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection—0
Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)—0
Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge—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