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 501–550 of 8378 papers

TitleStatusHype
Graph Contrastive Learning for Connectome ClassificationCode0
Importance Sampling via Score-based Generative Models—0
Reformulation for Pretraining Data Augmentation—0
YOLOv4: A Breakthrough in Real-Time Object Detection—0
Consistency of augmentation graph and network approximability in contrastive learningCode0
Understanding and Mitigating the Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks—0
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation—0
SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited LabelsCode1
TopoCL: Topological Contrastive Learning for Time Series—0
DILLEMA: Diffusion and Large Language Models for Multi-Modal AugmentationCode0
Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
The Skin Game: Revolutionizing Standards for AI Dermatology Model ComparisonCode0
RIE-SenseNet: Riemannian Manifold Embedding of Multi-Source Industrial Sensor Signals for Robust Pattern Recognition—0
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment—0
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting—0
Style transfer as data augmentation: evaluating unpaired image-to-image translation models in mammography—0
Learning Human Perception Dynamics for Informative Robot Communication—0
Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning ModelsCode0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Learning-Based TSP-Solvers Tend to Be Overly Greedy—0
Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data—0
Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonates—0
Lightspeed Geometric Dataset Distance via Sliced Optimal TransportCode0
Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities—0
Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach—0
Input layer regularization and automated regularization hyperparameter tuning for myelin water estimation using deep learningCode0
Synthetic Data Generation for Augmenting Small Samples—0
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling—0
Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarios—0
ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT ClassificationCode0
Misspellings in Natural Language Processing: A survey—0
Toward Efficient Generalization in 3D Human Pose Estimation via a Canonical Domain Approach—0
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
Optimizing Sentence Embedding with Pseudo-Labeling and Model Ensembles: A Hierarchical Framework for Enhanced NLP Tasks—0
Beyond In-Distribution Performance: A Cross-Dataset Study of Trajectory Prediction Robustness—0
Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating RoomCode0
CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image SegmentationCode0
TdAttenMix: Top-Down Attention Guided MixupCode0
Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models—0
Towards Robust Unsupervised Attention Prediction in Autonomous DrivingCode0
Inverse Evolution Data Augmentation for Neural PDE Solvers—0
A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques—0
PaMMA-Net: Plasmas magnetic measurement evolution based on data-driven incremental accumulative prediction—0
PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
Enhancing Medical Image Analysis through Geometric and Photometric transformations—0
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer 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