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 3901–3950 of 8378 papers

TitleStatusHype
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance—0
FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring—0
Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation—0
How to choose "Good" Samples for Text Data Augmentation—0
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
A Survey of Uncertainty in Deep Neural Networks—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
A survey of synthetic data augmentation methods in computer vision—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Foliar Uptake of Biocides: Statistical Assessment of Compartmental and Diffusion-Based Models—0
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation—0
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images—0
HpEIS: Learning Hand Pose Embeddings for Multimedia Interactive Systems—0
Focusing Image Generation to Mitigate Spurious Correlations—0
Contextual Scene Augmentation and Synthesis via GSACNet—0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data—0
A Graph Data Augmentation Strategy with Entropy Preservation—0
Adaptive Hardness-driven Augmentation and Alignment Strategies for Multi-Source Domain Adaptations—0
FMRI data augmentation via synthesis—0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps—0
Contextual Data Augmentation for Task-Oriented Dialog Systems—0
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification—0
FloorLevel-Net: Recognizing Floor-Level Lines with Height-Attention-Guided Multi-task Learning—0
Human-in-the-loop online multi-agent approach to increase trustworthiness in ML models through trust scores and data augmentation—0
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation—0
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation—0
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection—0
A Survey of Robust 3D Object Detection Methods in Point Clouds—0
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning—0
FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling—0
Flexible Mixture Modeling on Constrained Spaces—0
Human Vocal Sentiment Analysis—0
Context-gloss Augmentation for Improving Word Sense Disambiguation—0
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing—0
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges—0
Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006—0
Aggrotech: Leveraging Deep Learning for Sustainable Tomato Disease Management—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
Adaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility—0
HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation—0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction—0
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification—0
FKIMNet: A Finger Dorsal Image Matching Network Comparing Component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching—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