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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2951–3000 of 8378 papers

TitleStatusHype
Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology—0
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation—0
PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction—0
Can the accuracy bias by facial hairstyle be reduced through balancing the training data?—0
A Point-Neighborhood Learning Framework for Nasal Endoscope Image Segmentation—0
Symmetries in Overparametrized Neural Networks: A Mean-Field View—0
Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering—0
Mitigating annotation shift in cancer classification using single image generative modelsCode0
Weights Augmentation: it has never ever ever ever let her model downCode0
EntProp: High Entropy Propagation for Improving Accuracy and Robustness—0
Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement—0
EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision—0
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks—0
Data-augmented phrase-level alignment for mitigating object hallucination—0
NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning—0
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection—0
Arithmetic Reasoning with LLM: Prolog Generation & Permutation—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
DualContrast: Unsupervised Disentangling of Content and Transformations with Implicit Parameterization—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction—0
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness—0
TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification—0
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification—0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection—0
Planted: a dataset for planted forest identification from multi-satellite time series—0
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation—0
Synthetic Data Generation for Intersectional Fairness by Leveraging Hierarchical Group Structure—0
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers—0
Combining Euclidean Alignment and Data Augmentation for BCI decoding—0
Combining Denoising Autoencoders with Contrastive Learning to fine-tune Transformer ModelsCode0
Can LLMs Solve longer Math Word Problems Better?Code0
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models—0
Data Augmentation Techniques for Process Extraction from Scientific Publications—0
Data Augmentation Method Utilizing Template Sentences for Variable Definition Extraction—0
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification—0
Generalize Polyp Segmentation via Inpainting across Diverse Backgrounds and Pseudo-Mask RefinementCode0
Rumor Detection on Social Media with Reinforcement Learning-based Key Propagation Graph Generator—0
Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Data Augmentation for Text-based Person Retrieval Using Large Language Models—0
Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation—0
Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2—0
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation—0
Speech-dependent Data Augmentation for Own Voice Reconstruction with Hearable Microphones in Noisy Environments—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Team Samsung-RAL: Technical Report for 2024 RoboDrive Challenge-Robust Map Segmentation Track—0
Show:102550
← PrevPage 60 of 168Next →
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