Statements (62)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:bfsLayer |
8
|
| gptkbp:bfsParent |
gptkb:Cresnet_50
|
| gptkbp:competes_with |
other deep learning models
|
| gptkbp:developed_by |
gptkb:Kaiming_He
|
| gptkbp:has |
110 layers
|
| gptkbp:has_achievements |
high accuracy on CIFAR-10
|
| https://www.w3.org/2000/01/rdf-schema#label |
Cresnet 110
|
| gptkbp:introduced |
gptkb:2016
|
| gptkbp:is_a_tool_for |
research and development
|
| gptkbp:is_associated_with |
state-of-the-art results
|
| gptkbp:is_cited_in |
numerous academic papers
|
| gptkbp:is_compared_to |
traditional CN Ns
|
| gptkbp:is_designed_for |
image classification
|
| gptkbp:is_evaluated_by |
gptkb:Image_Net
cross-validation |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
gptkb:Keras gptkb:Py_Torch convolutional layers |
| gptkbp:is_influenced_by |
VGG Net
|
| gptkbp:is_known_for |
its depth and complexity
its scalability its modular architecture reducing overfitting solving vanishing gradient problem |
| gptkbp:is_optimized_for |
performance and efficiency
|
| gptkbp:is_part_of |
AI competitions
open-source projects Kaggle competitions deep learning research AI research community software development kits AI and machine learning curriculum |
| gptkbp:is_popular_in |
benchmarking neural networks
|
| gptkbp:is_referenced_in |
neural network design
|
| gptkbp:is_related_to |
gptkb:Dense_Net
|
| gptkbp:is_used_in |
gptkb:engine
real-time applications transfer learning feature extraction object detection tasks medical image analysis facial recognition systems computer vision tasks image segmentation tasks |
| gptkbp:performance |
new architectures
|
| gptkbp:related_model |
real-world applications
deep learning frameworks image recognition tasks feature learning. transfer learning applications |
| gptkbp:subject |
gptkb:document
research studies online courses academic courses machine learning conferences tutorials and workshops |
| gptkbp:training |
large datasets
backpropagation |
| gptkbp:uses |
skip connections
|
| gptkbp:variant |
gptkb:Residual_Networks
|