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 7051–7100 of 8378 papers

TitleStatusHype
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation—0
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation—0
Enhancing Facial Data Diversity with Style-based Face Aging—0
Learning Test-time Augmentation for Content-based Image Retrieval—0
Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models—0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
Enhancing Graph Contrastive Learning with Node Similarity—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Enhancing Indoor Temperature Forecasting through Synthetic Data in Low-Data Environments—0
Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images—0
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training—0
Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Enhancing Mathematical Reasoning in LLMs with Background Operators—0
Enhancing Medical Image Analysis through Geometric and Photometric transformations—0
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation—0
Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques—0
Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training—0
Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation—0
Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method—0
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation—0
Enhancing object detection robustness: A synthetic and natural perturbation approach—0
Enhancing Out-Of-Domain Utterance Detection with Data Augmentation Based on Word Embeddings—0
Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules—0
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions—0
Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster Contexts—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation—0
Enhancing Robustness of Human Detection Algorithms in Maritime SAR through Augmented Aerial Images to Simulate Weather Conditions—0
Enhancing Robustness of Vision-Language Models through Orthogonality Learning and Self-Regularization—0
Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks—0
Enhancing Romanian Offensive Language Detection through Knowledge Distillation, Multi-Task Learning, and Data Augmentation—0
CoCoSoDa: Effective Contrastive Learning for Code Search—0
Enhancing Spoofing Speech Detection Using Rhythm Information—0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation—0
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models—0
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN—0
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8—0
Enhancing Traffic Sign Recognition with Tailored Data Augmentation: Addressing Class Imbalance and Instance Scarcity—0
Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Invariance Principle Meets Vicinal Risk Minimization—0
Enlightenment Period Improving DNN Performance—0
Enrich the content of the image Using Context-Aware Copy Paste—0
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection—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