|
gptkbp:instance_of
|
gptkb:television_channel
|
|
gptkbp:bfsLayer
|
7
|
|
gptkbp:bfsParent
|
gptkb:Cresnet_2.0
|
|
gptkbp:architectural_style
|
gptkb:Residual_Network
|
|
gptkbp:contains
|
101 layers
|
|
gptkbp:developed_by
|
gptkb:Kaiming_He
|
|
gptkbp:has_variants
|
gptkb:Cresnet_152
gptkb:Cresnet_50
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Cresnet 101
|
|
gptkbp:improves
|
Gradient Flow
|
|
gptkbp:is_adopted_by
|
Startups
Tech Companies
|
|
gptkbp:is_compared_to
|
gptkb:Inception_Network
VGG Net
|
|
gptkbp:is_discussed_in
|
gptkb:Workshops
Conferences
|
|
gptkbp:is_documented_in
|
Research Papers
Technical Blogs
|
|
gptkbp:is_evaluated_by
|
gptkb:CIFAR-10
gptkb:CIFAR-100
Cross-Validation
Real-World Applications
Benchmark Datasets
Test Datasets
|
|
gptkbp:is_implemented_in
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
Deep Learning Techniques
Highway Networks
|
|
gptkbp:is_known_for
|
gptkb:Deep_Residual_Learning
Reducing Overfitting
Improving Training Speed
|
|
gptkbp:is_optimized_for
|
gptkb:benchmark
Accuracy
|
|
gptkbp:is_part_of
|
gptkb:Image_Net_Challenge
Deep Learning Models
Machine Learning Frameworks
AI Community
|
|
gptkbp:is_popular_in
|
Academic Research
Industry Applications
|
|
gptkbp:is_supported_by
|
NVIDIAGP Us
TP Us
|
|
gptkbp:is_used_by
|
gptkb:physicist
|
|
gptkbp:is_used_for
|
Image Classification
|
|
gptkbp:is_used_in
|
Image Recognition
Object Detection
Semantic Segmentation
|
|
gptkbp:performance
|
Computer Vision Tasks
Top-5 accuracy of 22.7% on Image Net
|
|
gptkbp:published_by
|
gptkb:2016
|
|
gptkbp:training
|
gptkb:Adam_Optimizer
gptkb:Image_Net_Dataset
Stochastic Gradient Descent
|
|
gptkbp:utilizes
|
Skip Connections
|