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 5701–5750 of 8378 papers

TitleStatusHype
A Simplified Framework for Contrastive Learning for Node Representations—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets—0
A Smartphone-Based Skin Disease Classification Using MobileNet CNN—0
ASMR: Augmenting Life Scenario using Large Generative Models for Robotic Action Reflection—0
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications—0
A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models—0
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data—0
A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition—0
Automatic Speech Recognition Advancements for Indigenous Languages of the Americas—0
ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understanding—0
ASRtrans at SemEval-2022 Task 4: Ensemble of Tuned Transformer-based Models for PCL Detection—0
Assessing Cardiomegaly in Dogs Using a Simple CNN Model—0
Assessing Dataset Bias in Computer Vision—0
Assessing Intra-class Diversity and Quality of Synthetically Generated Images in a Biomedical and Non-biomedical Setting—0
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Assessing the Feasibility of Internet-Sourced Video for Automatic Cattle Lameness Detection—0
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution—0
Assessing unconstrained surgical cuttings in VR using CNNs—0
Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance—0
Assessment Framework for Deepfake Detection in Real-world Situations—0
Learning Visual Representations with Optimum-Path Forest and its Applications to Barrett's Esophagus and Adenocarcinoma Diagnosis—0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation—0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty—0
A Study of Augmentation Methods for Handwritten Stenography Recognition—0
A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning—0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition—0
A study of the impact of generative AI-based data augmentation on software metadata classification—0
A Study of Transfer Learning in Music Source Separation—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
A study on cross-corpus speech emotion recognition and data augmentation—0
A Study on FGSM Adversarial Training for Neural Retrieval—0
A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification—0
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels—0
A supervised generative optimization approach for tabular data—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing—0
A Survey of Robust 3D Object Detection Methods in Point Clouds—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
A survey of synthetic data augmentation methods in computer vision—0
A Survey of Uncertainty in Deep Neural Networks—0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells—0
A Survey on Data Augmentation for Text Classification—0
A Survey on Data Synthesis and Augmentation for Large Language Models—0
A Survey on Deep Clustering: From the Prior Perspective—0
A Survey on Deep Domain Adaptation and Tiny Object Detection Challenges, Techniques and Datasets—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified