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.

Further readings:

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Papers

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Showing 1201–1250 of 8378 papers

TitleStatusHype
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language ModelsCode1
CARLA Drone: Monocular 3D Object Detection from a Different Perspective—0
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic SegmentationCode0
Explainable Deep Learning Framework for Human Activity Recognition—0
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning MethodsCode0
Automatic Image Annotation (AIA) of AlmondNet-20 Method for Almond Detection by Improved CNN-based Model—0
Generative AI in Industrial Machine Vision -- A Review—0
Speech Representation Learning Revisited: The Necessity of Separate Learnable Parameters and Robust Data Augmentation—0
SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised PretrainingCode1
Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series ForecastingCode1
Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups—0
A Closer Look at Data Augmentation Strategies for Finetuning-Based Low/Few-Shot Object Detection—0
Meta-Learning in Audio and Speech Processing: An End to End Comprehensive Review—0
Machine Learning with Physics Knowledge for Prediction: A Survey—0
Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer—0
Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise—0
Structure-enhanced Contrastive Learning for Graph Clustering—0
TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and DynamicsCode1
SZU-AFS Antispoofing System for the ASVspoof 5 Challenge—0
ARMADA: Attribute-Based Multimodal Data Augmentation—0
Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network—0
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts—0
Unsupervised Transfer Learning via Adversarial Contrastive Training—0
Generative Dataset Distillation Based on Diffusion ModelCode1
Action Recognition for Privacy-Preserving Ambient Assisted LivingCode0
WavLM model ensemble for audio deepfake detectionCode0
OMR: Occlusion-Aware Memory-Based Refinement for Video Lane DetectionCode0
How to Solve Contextual Goal-Oriented Problems with Offline Datasets?Code0
Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD—0
Leveraging Priors via Diffusion Bridge for Time Series Generation—0
Temporal Variability and Multi-Viewed Self-Supervised Representations to Tackle the ASVspoof5 Deepfake Challenge—0
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization—0
Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies—0
BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data TrainingCode2
ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers—0
LaiDA: Linguistics-aware In-context Learning with Data Augmentation for Metaphor Components IdentificationCode0
A Recurrent YOLOv8-based framework for Event-Based Object Detection—0
reCSE: Portable Reshaping Features for Sentence Embedding in Self-supervised Contrastive LearningCode0
Model Debiasing by Learnable Data Augmentation—0
Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text GuidanceCode1
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
PushPull-Net: Inhibition-driven ResNet robust to image corruptionsCode0
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image SegmentationCode0
VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMsCode1
RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution NetworksCode2
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data AugmentationCode0
Training-Free Condition Video Diffusion Models for single frame Spatial-Semantic Echocardiogram SynthesisCode1
Diverse Generation while Maintaining Semantic Coordination: A Diffusion-Based Data Augmentation Method for Object 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