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 2301–2350 of 8378 papers

TitleStatusHype
Improving the performance of weak supervision searches using data augmentation—0
CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures—0
Reverse Thinking Makes LLMs Stronger Reasoners—0
Topology-Preserving Scaling in Data Augmentation—0
UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation—0
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection—0
Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification—0
Thai Financial Domain Adaptation of THaLLE -- Technical Report—0
Task Progressive Curriculum Learning for Robust Visual Question Answering—0
Scaling nnU-Net for CBCT Segmentation—0
Breast Tumor Classification Using EfficientNet Deep Learning ModelCode0
Semantic Data Augmentation for Long-tailed Facial Expression Recognition—0
SynDiff-AD: Improving Semantic Segmentation and End-to-End Autonomous Driving with Synthetic Data from Latent Diffusion Models—0
J-CaPA : Joint Channel and Pyramid Attention Improves Medical Image Segmentation—0
Unsupervised Event Outlier Detection in Continuous Time—0
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches—0
RoCoDA: Counterfactual Data Augmentation for Data-Efficient Robot Learning from Demonstrations—0
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification—0
Machine-agnostic Automated Lumbar MRI Segmentation using a Cascaded Model Based on Generative Neurons—0
Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers—0
Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding—0
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural NetworkCode0
Evaluating LLM Prompts for Data Augmentation in Multi-label Classification of Ecological Texts—0
MVANet: Multi-Stage Video Attention Network for Sound Event Localization and Detection with Source Distance EstimationCode0
Improving Routability Prediction via NAS Using a Smooth One-shot Augmented Predictor—0
An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains—0
Next-Generation Phishing: How LLM Agents Empower Cyber Attackers—0
SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers—0
Simulating Liquidity: Agent-Based Modeling of Illiquid Markets for Fractional Ownership—0
Conditional Distribution Learning on GraphsCode0
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM—0
On the Way to LLM Personalization: Learning to Remember User Conversations—0
Whisper Finetuning on Nepali Language—0
Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification—0
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production—0
Can Open-source LLMs Enhance Data Synthesis for Toxic Detection?: An Experimental Study—0
Online Item Cold-Start Recommendation with Popularity-Aware Meta-LearningCode0
Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network—0
Variational Bayesian Bow tie Neural Networks with ShrinkageCode0
LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed Multi-Label Classification and Fairness Challenges—0
Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion ModelCode0
Enhancing PTSD Outcome Prediction with Ensemble Models in Disaster Contexts—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathologyCode0
Bayesian estimation of finite mixtures of Tobit models—0
Generative AI for Data Augmentation in Wireless Networks: Analysis, Applications, and Case StudyCode0
Optimal Transport-Based Displacement Interpolation with Data Augmentation for Reduced Order Modeling of Nonlinear Dynamical Systems—0
A Heterogeneous Graph Neural Network Fusing Functional and Structural Connectivity for MCI Diagnosis—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