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 5901–5950 of 8378 papers

TitleStatusHype
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation—0
Awareness of uncertainty in classification using a multivariate model and multi-views—0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis—0
Background Mixup Data Augmentation for Hand and Object-in-Contact Detection—0
Back-Translation-Style Data Augmentation for End-to-End ASR—0
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation—0
Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity—0
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays—0
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data—0
Baidu Neural Machine Translation Systems for WMT19—0
BaIT: Barometer for Information Trustworthiness—0
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance—0
Balanced Masked and Standard Face Recognition—0
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production—0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Balancing Data through Data Augmentation Improves the Generality of Transfer Learning for Diabetic Retinopathy Classification—0
Banana Sub-Family Classification and Quality Prediction using Computer Vision—0
BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali—0
Basque and Spanish Counter Narrative Generation: Data Creation and Evaluation—0
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation—0
Bayesian Analysis of Dynamic Linear Topic Models—0
Bayesian CART models for insurance claims frequency—0
Bayesian estimation of finite mixtures of Tobit models—0
Bayesian Generative Active Deep Learning—0
Bayesian Inference for Gamma Models—0
Data Augementation with Polya Inverse Gamma—0
Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP—0
Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling—0
Bayesian Semi-supervised Multi-category Classification under Nonparanormality—0
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation—0
BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions—0
BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading—0
Bemba Speech Translation: Exploring a Low-Resource African Language—0
Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios—0
Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning—0
Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise—0
Benchmarking Machine Learning Methods for Distributed Acoustic Sensing—0
Benchmarking Robustness in Neural Radiance Fields—0
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance—0
Benchmarking zero-shot and few-shot approaches for tokenization, tagging, and dependency parsing of Tagalog text—0
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content—0
Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach—0
Bengali Document Layout Analysis with Detectron2—0
“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?—0
Benign Adversarial Attack: Tricking Models for Goodness—0
BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classification—0
BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classification—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Best Practices in Pool-based Active Learning for Image Classification—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