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 3751–3800 of 8378 papers

TitleStatusHype
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs—0
Robust Stance Detection: Understanding Public Perceptions in Social Media—0
Boosting High Resolution Image Classification with Scaling-up TransformersCode0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''—0
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training—0
DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics—0
Proposing an intelligent mesh smoothing method with graph neural networks—0
Order-preserving Consistency Regularization for Domain Adaptation and GeneralizationCode0
Attention Is All You Need For Blind Room Volume Estimation—0
COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs—0
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks—0
EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition—0
AMPLIFY:Attention-based Mixup for Performance Improvement and Label Smoothing in TransformerCode0
Improving Generalization in Game Agents with Data Augmentation in Imitation Learning—0
Deepfake audio as a data augmentation technique for training automatic speech to text transcription models—0
Adaptive Input-image Normalization for Solving the Mode Collapse Problem in GAN-based X-ray Images—0
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports—0
Using Artificial Intelligence for the Automation of Knitting Patterns—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving—0
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation—0
PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding—0
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series DataCode0
Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
Tightening Classification Boundaries in Open Set Domain Adaptation through Unknown Exploitation—0
Enhancing Visual Perception in Novel Environments via Incremental Data Augmentation Based on Style TransferCode0
Improve Deep Forest with Learnable Layerwise Augmentation Policy ScheduleCode0
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation—0
A Stochastic Online Forecast-and-Optimize Framework for Real-Time Energy Dispatch in Virtual Power Plants under Uncertainty—0
Towards Robust and Smooth 3D Multi-Person Pose Estimation from Monocular Videos in the Wild—0
Detail Reinforcement Diffusion Model: Augmentation Fine-Grained Visual Categorization in Few-Shot Conditions—0
Leveraging the Power of Data Augmentation for Transformer-based Tracking—0
Data Distribution Bottlenecks in Grounding Language Models to Knowledge BasesCode0
Codec Data Augmentation for Time-domain Heart Sound Classification—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic ManipulationCode0
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
SC-MAD: Mixtures of Higher-order Networks for Data Augmentation—0
Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images—0
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition—0
The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detectionCode0
Data Augmentation via Subgroup Mixup for Improving Fairness—0
In-Contextual Gender Bias Suppression for Large Language ModelsCode0
Limitations of Face Image GenerationCode0
Balanced and Explainable Social Media Analysis for Public Health with Large Language ModelsCode0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method—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