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.

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Papers

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Showing 3451–3500 of 8378 papers

TitleStatusHype
FRED: Towards a Full Rotation-Equivariance in Aerial Image Object Detection—0
FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation—0
Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Graph-Preserving Grid Layout: A Simple Graph Drawing Method for Graph Classification using CNNs—0
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification—0
Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization—0
Disease Severity Regression with Continuous Data Augmentation—0
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine—0
From Spelling to Grammar: A New Framework for Chinese Grammatical Error Correction—0
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization—0
Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions—0
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips—0
FROTE: Feedback Rule-Driven Oversampling for Editing Models—0
Frozen Feature Augmentation for Few-Shot Image Classification—0
Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images—0
Adversarial AutoAugment—0
Discriminative Reranking for Neural Machine Translation—0
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene—0
FSDNet-An efficient fire detection network for complex scenarios based on YOLOv3 and DenseNet—0
Contrastive learning for unsupervised medical image clustering and reconstruction—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance, Head-pose, and Facial Expression Features—0
FSRT: Facial Scene Representation Transformer for Face Reenactment from Factorized Appearance Head-pose and Facial Expression Features—0
Contrastive Learning from Pairwise Measurements—0
A Survey on Face Data Augmentation—0
Discriminative Relational Topic Models—0
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences—0
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models—0
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—0
Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study—0
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
Fully Bayesian inference for neural models with negative-binomial spiking—0
Fully Test-time Adaptation for Tabular Data—0
A Survey on Neural Architecture Search—0
A Comprehensive Augmentation Framework for Anomaly Detection—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
Further advantages of data augmentation on convolutional neural networks—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified