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Data Augmentation

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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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Data Augmentation

논문

Data augmentation using generative networks to identify dementia

Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network

Stay on the Manifold: Hierarchical Bayesian Trajectory-Family Transfer for Sparse-Target Reconstruction and Sampling

SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations

Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images

Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management

Similarity-Guided Diffusion for Contrastive Sequential Recommendation

Data Augmentation in Time Series Forecasting through Inverted Framework

Iceberg: Enhancing HLS Modeling with Synthetic Data

AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation

PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning

TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems

Semantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation

Evolution without Large Models: Training Language Model with Task Principles

Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis

Prompt-Free Conditional Diffusion for Multi-object Image Augmentation

DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation

Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques

Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums

DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback Synergy

Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation

HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation

Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges

Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels

GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models

rQdia: Regularizing Q-Value Distributions With Image Augmentation

Leveraging AI Graders for Missing Score Imputation to Achieve Accurate Ability Estimation in Constructed-Response Tests

Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation

RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation

Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps

Cross-regularization: Adaptive Model Complexity through Validation Gradients

Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization

DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation

On the Robustness of Human-Object Interaction Detection against Distribution Shift