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 2651–2700 of 8378 papers

TitleStatusHype
Diffusion Model with Clustering-based Conditioning for Food Image Generation—0
Will sentiment analysis need subculture? A new data augmentation approachCode0
Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules—0
Prediction of Diblock Copolymer Morphology via Machine Learning—0
The Gender-GAP Pipeline: A Gender-Aware Polyglot Pipeline for Gender Characterisation in 55 LanguagesCode2
Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image SegmentationCode0
Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy DataCode0
Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation—0
Classification robustness to common optical aberrationsCode0
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning—0
On the Robustness of Object Detection Models on Aerial ImagesCode1
Adapting Text-based Dialogue State Tracker for Spoken Dialogues—0
From SMOTE to Mixup for Deep Imbalanced ClassificationCode1
A Comprehensive Augmentation Framework for Anomaly Detection—0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Tackling Diverse Minorities in Imbalanced Classification—0
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities—0
Semi-Supervised Learning for Visual Bird's Eye View Semantic SegmentationCode1
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework—0
A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy—0
Handwritten image augmentation—0
Bengali Document Layout Analysis with Detectron2—0
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent DetectionCode0
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere—0
A Systematic Study on Quantifying Bias in GAN-Augmented Data—0
Cross-Modality Proposal-guided Feature Mining for Unregistered RGB-Thermal Pedestrian Detection—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in RecommendationCode1
Random Word Data Augmentation with CLIP for Zero-Shot Anomaly Detection—0
Targeted Data Augmentation for bias mitigation—0
Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight AveragingCode1
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection—0
Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial NetworksCode0
TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric PerspectiveCode1
Distributionally Robust Cross Subject EEG Decoding—0
An Empirical Study of CLIP for Text-based Person SearchCode1
Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets—0
ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious CorrelationsCode0
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation—0
Robust Fraud Detection via Supervised Contrastive Learning—0
CCFace: Classification Consistency for Low-Resolution Face Recognition—0
Generative Machine Listener—0
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data—0
Data augmentation and explainability for bias discovery and mitigation in deep learning—0
A tailored Handwritten-Text-Recognition System for Medieval Latin—0
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRICode1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image SegmentationCode0
MixBag: Bag-Level Data Augmentation for Learning from Label Proportions—0
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays—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