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 6151–6200 of 8378 papers

TitleStatusHype
Class balanced underwater object detection dataset generated by class-wise style augmentation—0
Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Classification Confidence Estimation with Test-Time Data-Augmentation—0
Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation—0
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics—0
Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks—0
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network—0
Classifying COVID-19 vaccine narratives—0
Classifying cow stall numbers using YOLO—0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
Clean Evaluations on Contaminated Visual Language Models—0
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach—0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift—0
Clothes Grasping and Unfolding Based on RGB-D Semantic Segmentation—0
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Clozer”:" Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs—0
Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysis—0
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task—0
Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images—0
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients—0
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation—0
CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset—0
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation—0
CNN-powered micro- to macro-scale flow modeling in deformable porous media—0
CNNs Avoid Curse of Dimensionality by Learning on Patches—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
Coarse-to-fine Task-driven Inpainting for Geoscience Images—0
COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs—0
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding—0
Codec Data Augmentation for Time-domain Heart Sound Classification—0
Code Execution with Pre-trained Language Models—0
CodeFort: Robust Training for Code Generation Models—0
Code Needs Comments: Enhancing Code LLMs with Comment Augmentation—0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition—0
Code-Switching without Switching: Language Agnostic End-to-End Speech Translation—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Cognitive Biases in Large Language Models for News Recommendation—0
Cold Start Streaming Learning for Deep Networks—0
ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images—0
ColorUNet: A convolutional classification approach to colorization—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—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