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.

Further readings:

( Image credit: Albumentations )

Papers

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Showing 3351–3400 of 8378 papers

TitleStatusHype
Few-shot Mining of Naturally Occurring Inputs and Outputs—0
Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration—0
Few-Shot Object Detection in Real Life: Case Study on Auto-Harvest—0
Conditional Synthetic Food Image Generation—0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
Bootstrapping User and Item Representations for One-Class Collaborative Filtering—0
Field-of-View IoU for Object Detection in 360° Images—0
A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation—0
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
"Generative Models for Financial Time Series Data: Enhancing Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market—0
A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation—0
Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices—0
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect—0
Finance document Extraction Using Data Augmentation and Attention—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
Finding and Fixing Spurious Patterns with Explanations—0
Finding NeMo: Negative-mined Mosaic Augmentation for Referring Image Segmentation—0
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks—0
Findings of the Second Workshop on Neural Machine Translation and Generation—0
Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning—0
Bootstrapping a User-Centered Task-Oriented Dialogue System—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases—0
Fine-grained building roof instance segmentation based on domain adapted pretraining and composite dual-backbone—0
A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education—0
Fine-Grained Few Shot Learning with Foreground Object Transformation—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization—0
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis—0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition—0
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset—0
Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Real-Time Helmet Violation Detection in AI City Challenge 2023 with Genetic Algorithm-Enhanced YOLOv5—0
Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology—0
Generative Models for Multi-Illumination Color Constancy—0
GenLabel: Mixup Relabeling using Generative Models—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Atomic Activity Recognition 2024—0
First Place Solution to the ECCV 2024 ROAD++ Challenge @ ROAD++ Spatiotemporal Agent Detection 2024—0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI—0
Fish Detection Using Deep Learning—0
Fish-TViT: A novel fish species classification method in multi water areas based on transfer learning and vision transformer—0
Disease Severity Regression with Continuous Data Augmentation—0
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine—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