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 5301–5350 of 8378 papers

TitleStatusHype
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning—0
AdaNN: Adaptive Neural Network-based Equalizer via Online Semi-supervised Learning—0
ADAPT at SR’20: How Preprocessing and Data Augmentation Help to Improve Surface Realization—0
Adapting Abstract Meaning Representation Parsing to the Clinical Narrative -- the SPRING THYME parser—0
Adapting Coreference Resolution for Processing Violent Death Narratives—0
Adapting Multilingual Models for Code-Mixed Translation using Back-to-Back Translation—0
Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective—0
Adapting Text-based Dialogue State Tracker for Spoken Dialogues—0
Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement—0
Adaptive Data Augmentation for Contrastive Learning—0
Adaptive Data Augmentation for Thompson Sampling—0
Adaptive Data Augmentation on Temporal Graphs—0
Adaptive Data Augmentation with Deep Parallel Generative Models—0
Adaptive Feature Selection for End-to-End Speech Translation—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations—0
Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack—0
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images—0
Adaptive Label Smoothing for Out-of-Distribution Detection—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
Adaptive Neural Networks for Intelligent Data-Driven Development—0
Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models—0
Adaptive Regularization of Labels—0
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation—0
Adaptive Unbiased Teacher for Cross-Domain Object Detection—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks—0
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments—0
A Data Augmentation Method and the Embedding Mechanism for Detection and Classification of Pulmonary Nodules on Small Samples—0
Data augmentation by morphological mixup for solving Raven's Progressive Matrices—0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
A data augmentation methodology for training machine/deep learning gait recognition algorithms—0
Image retrieval outperforms diffusion models on data augmentation—0
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN—0
A data augmentation strategy for deep neural networks with application to epidemic modelling—0
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection—0
A data-centric approach to class-specific bias in image data augmentation—0
A Data-Driven Analysis of Robust Automatic Piano Transcription—0
A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors—0
A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization—0
AdaTransform: Adaptive Data Transformation—0
Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift—0
AdBooster: Personalized Ad Creative Generation using Stable Diffusion Outpainting—0
ADD: Attribution-Driven Data Augmentation Framework for Boosting Image Super-Resolution—0
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models—0
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification—0
Addressing degeneracies in latent interpolation for diffusion models—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