Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:adapted_into |
Various Input Sizes
|
| gptkbp:applies_to |
Medical Imaging
|
| gptkbp:based_on |
gptkb:Alumni_Association
|
| gptkbp:benefits |
Noisy Data
|
| gptkbp:can_be_extended_by |
Other Attention Mechanisms
|
| gptkbp:challenges |
Train Without Overfitting
|
| gptkbp:developed_by |
gptkb:Olaf_Ronneberger
|
| gptkbp:enhances |
Feature Extraction
|
| https://www.w3.org/2000/01/rdf-schema#label |
U-Net with Attention
|
| gptkbp:improves |
Segmentation Accuracy
|
| gptkbp:introduced |
gptkb:2015
|
| gptkbp:is_characterized_by |
Skip Connections
Downsampling Layers Upsampling Layers |
| gptkbp:is_compared_to |
gptkb:Standard_U-Net
|
| gptkbp:is_designed_for |
Image Segmentation
|
| gptkbp:is_effective_against |
Real-Time Applications
|
| gptkbp:is_evaluated_by |
Cross-Validation
Benchmark Datasets |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch |
| gptkbp:is_optimized_for |
GPU Acceleration
|
| gptkbp:is_popular_in |
Research Papers
Deep Learning Community |
| gptkbp:is_used_for |
Semantic Segmentation
|
| gptkbp:is_used_in |
gptkb:healthcare_organization
gptkb:software gptkb:museum Autonomous Driving Biometrics Medical Diagnosis Object Detection Facial Recognition Image-to-Image Translation Agricultural Monitoring Histopathology Tumor Detection Image Restoration Video Analysis Text Recognition Satellite Image Analysis 3 D Reconstruction Cell Segmentation Organ Segmentation Pathology Analysis |
| gptkbp:requires |
Less Training Data
|
| gptkbp:utilizes |
Attention Mechanism
|
| gptkbp:bfsParent |
gptkb:Attention_U-Net
|
| gptkbp:bfsLayer |
4
|