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 4001–4050 of 8378 papers

TitleStatusHype
Improving the Effectiveness of Deep Generative Data—0
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture—0
Image compositing is all you need for data augmentation—0
Image Data Augmentation for Deep Learning: A Survey—0
Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks—0
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations—0
Image Ordinal Classification and Understanding: Grid Dropout with Masking Label—0
Data Augmentation Can Improve Robustness—0
BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering—0
Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data—0
BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation—0
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering—0
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases—0
Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network—0
BIT-Xiaomi’s System for AutoSimTrans 2022—0
Imbalanced Sentiment Classification Enhanced with Discourse Marker—0
A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages—0
StackMix: A complementary Mix algorithm—0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera—0
3D Transformer based on deformable patch location for differential diagnosis between Alzheimer's disease and Frontotemporal dementia—0
An Exploration of Data Augmentation Techniques for Improving English to Tigrinya Translation—0
Impact of Adversarial Training on Robustness and Generalizability of Language Models—0
Impact of Aliasing on Generalization in Deep Convolutional Networks—0
Impact of Data Augmentation on QCNNs—0
Impact of Dataset on Acoustic Models for Automatic Speech Recognition—0
A comparison of streaming models and data augmentation methods for robust speech recognition—0
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation—0
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction—0
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning—0
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis—0
Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams—0
Data augmentation for dealing with low sampling rates in NILM—0
Implicit Counterfactual Data Augmentation for Robust Learning—0
Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation—0
Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)—0
Developing the Reliable Shallow Supervised Learning for Thermal Comfort using ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II—0
Developing neural machine translation models for Hungarian-English—0
An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization—0
Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre-trained convolutional neural networks—0
Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques—0
Importance of Data Loading Pipeline in Training Deep Neural Networks—0
Improving speaker verification robustness with synthetic emotional utterances—0
Developing a Component Comment Extractor from Product Reviews on E-Commerce Sites—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Deterministic Certification to Adversarial Attacks via Bernstein Polynomial Approximation—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