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 6401–6450 of 8378 papers

TitleStatusHype
CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting—0
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation—0
Cyclic Test Time Augmentation with Entropy Weight Method—0
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection—0
DAAS: Differentiable Architecture and Augmentation Policy Search—0
DACB-Net: Dual Attention Guided Compact Bilinear Convolution Neural Network for Skin Disease Classification—0
DaCy: A Unified Framework for Danish NLP—0
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks—0
DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)—0
DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks—0
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase—0
DAKD: Data Augmentation and Knowledge Distillation using Diffusion Models for SAR Oil Spill Segmentation—0
DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization—0
Dartmouth at SemEval-2022 Task 6: Detection of Sarcasm—0
DARTSRepair: Core-failure-set Guided DARTS for Network Robustness to Common Corruptions—0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification—0
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation—0
DAST: Difficulty-Aware Self-Training on Large Language Models—0
Data-Algorithm-Architecture Co-Optimization for Fair Neural Networks on Skin Lesion Dataset—0
Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming—0
Data Augmentation and Classification of Sea-Land Clutter for Over-the-Horizon Radar Using AC-VAEGAN—0
Data Augmentation and Clustering for Vehicle Make/Model Classification—0
Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset—0
Data augmentation and explainability for bias discovery and mitigation in deep learning—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Data augmentation and image understanding—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation—0
Data Augmentation and Squeeze-and-Excitation Network on Multiple Dimension for Sound Event Localization and Detection in Real Scenes—0
Data Augmentation and Terminology Integration for Domain-Specific Sinhala-English-Tamil Statistical Machine Translation—0
Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition—0
Data augmentation approaches for improving animal audio classification—0
Data Augmentation as Feature Manipulation—0
Data augmentation as stochastic optimization—0
How Data Augmentation affects Optimization for Linear Regression—0
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Data Augmentation Based on Distributed Expressions in Text Classification Tasks—0
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding—0
Data Augmentation by Pairing Samples for Images Classification—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Data Augmentation Can Improve Robustness—0
Data Augmentation for Automated Adaptive Rodent Training—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