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 6551–6600 of 8378 papers

TitleStatusHype
Data Augmentations in Deep Weight Spaces—0
Data Augmentation Strategies for Improving Sequential Recommender Systems—0
Data Augmentation Techniques for Machine Translation of Code-Switched Texts: A Comparative Study—0
Data Augmentation Techniques for Process Extraction from Scientific Publications—0
Data augmentation techniques for the Video Question Answering task—0
Data Augmentation techniques in time series domain: A survey and taxonomy—0
Data Augmentation Technology for Dysarthria Assistive Systems—0
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation—0
Data Augmentation Through Random Style Replacement—0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Data augmentation to improve robustness of image captioning solutions—0
Data Augmentation Using Adversarial Training for Construction-Equipment Classification—0
Data augmentation using back-translation for context-aware neural machine translation—0
Data augmentation using diffusion models to enhance inverse Ising inference—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
Data augmentation using generative networks to identify dementia—0
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges—0
Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language—0
Data Augmentation Using Neural Acoustic Fields With Retrieval-Augmented Pre-training—0
Data Augmentation using Random Image Cropping for High-resolution Virtual Try-On (VITON-CROP)—0
Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification—0
Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments—0
Data Augmentation via Diffusion Model to Enhance AI Fairness—0
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery—0
Data Augmentation via Structured Adversarial Perturbations—0
Data Augmentation via Subgroup Mixup for Improving Fairness—0
Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages—0
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Data Augmentation with Dual Training for Offensive Span Detection—0
Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset—0
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing—0
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving—0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition—0
Data Augmentation with Manifold Barycenters—0
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness—0
Data augmentation with mixtures of max-entropy transformations for filling-level classification—0
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System—0
Data Augmentation with Sentence Recombination Method for Semi-supervised Text Classification—0
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Data-Augmented Counterfactual Learning for Bundle Recommendation—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Data centric approach to Chinese Medical Speech Recognition—0
Data-driven Approaches to Surrogate Machine Learning Model Development—0
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis—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