|
gptkbp:instance_of
|
gptkb:microprocessor
|
|
gptkbp:bfsLayer
|
5
|
|
gptkbp:bfsParent
|
gptkb:Atrous_Convolution
gptkb:Res_Ne_Xt
gptkb:Dense_Net-264
|
|
gptkbp:applies_to
|
Object Detection
Image Segmentation
Video Analysis
|
|
gptkbp:developed_by
|
gptkb:Kaiming_He
|
|
gptkbp:has_achievements
|
State-of-the-art Performance
|
|
gptkbp:has_variants
|
gptkb:Res_Net-101
gptkb:Res_Net-152
gptkb:Res_Net-50
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Residual Networks
|
|
gptkbp:improves
|
Training of Deep Networks
|
|
gptkbp:introduced
|
gptkb:2015
|
|
gptkbp:is_evaluated_by
|
gptkb:Image_Net
gptkb:CIFAR-10
gptkb:COCO_Dataset
F1 Score
Mean Average Precision
Top-1 Accuracy
Top-5 Accuracy
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:Dense_Net
Highway Networks
|
|
gptkbp:is_known_for
|
gptkb:Deep_Residual_Learning
Scalability
High Accuracy
Layer-wise Learning Rate
Mitigating Vanishing Gradient Problem
Residual Learning Framework
|
|
gptkbp:is_part_of
|
gptkb:Artificial_Intelligence
gptkb:software_framework
gptkb:Deep_Learning
Research in AI
Research in ML
|
|
gptkbp:is_popular_in
|
gptkb:viewpoint
Deep Learning Competitions
|
|
gptkbp:is_related_to
|
gptkb:television_channel
gptkb:Batch_Normalization
gptkb:Neural_Architecture_Search
Dropout Regularization
Transfer Learning Techniques
|
|
gptkbp:is_used_for
|
Feature Extraction
Fine-tuning Models
|
|
gptkbp:is_used_in
|
gptkb:streaming_service
gptkb:Autonomous_Vehicles
gptkb:software
gptkb:software_framework
gptkb:studio
Facial Recognition
Generative Models
Medical Image Analysis
|
|
gptkbp:training
|
gptkb:Adam_Optimizer
Backpropagation
Stochastic Gradient Descent
|
|
gptkbp:used_in
|
Image Classification
|
|
gptkbp:uses
|
Skip Connections
|