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 7101–7150 of 8378 papers

TitleStatusHype
Ensemble of pre-trained language models and data augmentation for hate speech detection from Arabic tweets—0
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection—0
Ensuring Equitable Financial Decisions: Leveraging Counterfactual Fairness and Deep Learning for Bias—0
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks—0
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding—0
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching—0
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models—0
EntProp: High Entropy Propagation for Improving Accuracy and Robustness—0
EnvGAN: Adversarial Synthesis of Environmental Sounds for Data Augmentation—0
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network—0
Environment Transfer for Distributed Systems—0
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost—0
EPICURE Ensemble Pretrained Models for Extracting Cancer Mutations from Literature—0
EPYNET: Efficient Pyramidal Network for Clothing Segmentation—0
Equirectangular image construction method for standard CNNs for Semantic Segmentation—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
Equivariant Neural Tangent Kernels—0
Equivariant score-based generative models provably learn distributions with symmetries efficiently—0
Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model—0
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model—0
eRock at Qur’an QA 2022: Contemporary Deep Neural Networks for Qur’an based Reading Comprehension Question Answers—0
ERUP-YOLO: Enhancing Object Detection Robustness for Adverse Weather Condition by Unified Image-Adaptive Processing—0
ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning—0
Estimating Input Coefficients for Regional Input-Output Tables Using Deep Learning with Mixup—0
Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images—0
Estimating Traffic Speeds using Probe Data: A Deep Neural Network Approach—0
Estimating Uncertainty in Landslide Segmentation Models—0
EucliDreamer: Fast and High-Quality Texturing for 3D Models with Stable Diffusion Depth—0
Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization—0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts—0
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference—0
Evaluating a Synthetic Image Dataset Generated with Stable Diffusion—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images—0
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks—0
Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification—0
Evaluating Deep Music Generation Methods Using Data Augmentation—0
Evaluating Large Language Models for Health-Related Text Classification Tasks with Public Social Media Data—0
Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
Evaluating Neural Networks for Early Maritime Threat Detection—0
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift—0
Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Evaluating the Impact of Data Augmentation on Predictive Model Performance—0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing—0
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
Evaluation Metrics for Text Data Augmentation in NLP—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