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 2551–2600 of 8378 papers

TitleStatusHype
AraBench: Benchmarking Dialectal Arabic-English Machine Translation—0
Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation—0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals—0
Enhance Visual Recognition under Adverse Conditions via Deep Networks—0
Cascaded Generation of High-quality Color Visible Face Images from Thermal Captures—0
Cascaded Diffusion Models for High Fidelity Image Generation—0
A quantifiable testing of global translational invariance in Convolutional and Capsule Networks—0
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics—0
A Quality-Centric Framework for Generic Deepfake Detection—0
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes—0
Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records—0
CarveNet: Carving Point-Block for Complex 3D Shape Completion—0
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications—0
AquaFuse: Waterbody Fusion for Physics Guided View Synthesis of Underwater Scenes—0
end-to-end training of a large vocabulary end-to-end speech recognition system—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment—0
CARLA Drone: Monocular 3D Object Detection from a Different Perspective—0
A PubMedBERT-based Classifier with Data Augmentation Strategy for Detecting Medication Mentions in Tweets—0
Cardiac Segmentation of LGE MRI with Noisy Labels—0
Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning—0
APT: Adaptive Personalized Training for Diffusion Models with Limited Data—0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
A proximal policy optimization based intelligent home solar management—0
A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects—0
Capturing Variabilities from Computed Tomography Images with Generative Adversarial Networks—0
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training—0
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020—0
English-Russian Data Augmentation for Neural Machine Translation—0
Capsule Network Performance on Complex Data—0
Adversarial Policy Optimization in Deep Reinforcement Learning—0
Capsule Deep Neural Network for Recognition of Historical Graffiti Handwriting—0
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training—0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
End-to-End Speech Recognition with High-Frame-Rate Features Extraction—0
Can We Improve Model Robustness through Secondary Attribute Counterfactuals?—0
A Pre-trained Data Deduplication Model based on Active Learning—0
Can We Generate Visual Programs Without Prompting LLMs?—0
A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
End-to-End Speech Translation of Arabic to English Broadcast News—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability—0
End-to-end Neural Diarization: From Transformer to Conformer—0
CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures—0
A Preliminary Study on Data Augmentation of Deep Learning for Image Classification—0
End-to-end neural networks for subvocal speech recognition—0
Can the accuracy bias by facial hairstyle be reduced through balancing the training data?—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—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