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Image Augmentation
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Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.
Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing
( Image credit: Kornia )
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
rQdia: Regularizing Q-Value Distributions With Image Augmentation
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
Camera-based method for the detection of lifted truck axles using convolutional neural networks
When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder
Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection
Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching
Diffusion Models for Robotic Manipulation: A Survey
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations
A framework for river connectivity classification using temporal image processing and attention based neural networks
Deep Ensembling with Multimodal Image Fusion for Efficient Classification of Lung Cancer
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A Survey
Multi-visual modality micro drone-based structural damage detection
deepTerra -- AI Land Classification Made Easy
Siamese Networks for Cat Re-Identification: Exploring Neural Models for Cat Instance Recognition
Image Augmentation Agent for Weakly Supervised Semantic Segmentation
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations
VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image Enhancement
Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation
Hybrid GAN and Fourier Transformation for SAR Ocean Pattern Image Augmentation
BGM: Background Mixup for X-ray Prohibited Items Detection
Enhancing weed detection performance by means of GenAI-based image augmentation
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts
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