|
gptkbp:instance_of
|
gptkb:neural_networks
|
|
gptkbp:architecture
|
gptkb:Feedforward_Neural_Network
|
|
gptkbp:developed_by
|
Gao Huang
|
|
gptkbp:has_achieved
|
State-of-the-art Performance
|
|
gptkbp:has_applications_in
|
Object Detection
Facial Recognition
Image Segmentation
Medical Imaging
Video Analysis
|
|
gptkbp:has_limitations
|
Memory Usage
Training Time
Computational Cost
|
|
gptkbp:has_variants
|
gptkb:Dense_Net-121
gptkb:Dense_Net-169
gptkb:Dense_Net-201
gptkb:Dense_Net-264
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Dense Net
|
|
gptkbp:improves
|
Traditional CNNs
|
|
gptkbp:is_based_on
|
Residual Learning
|
|
gptkbp:is_compared_to
|
gptkb:Inception
gptkb:Res_Net
|
|
gptkbp:is_evaluated_by
|
gptkb:test_subjects
gptkb:Image_Net
gptkb:CIFAR-10
gptkb:CIFAR-100
Cross-Validation
|
|
gptkbp:is_implemented_in
|
gptkb:Tensor_Flow
gptkb:Py_Torch
|
|
gptkbp:is_influenced_by
|
gptkb:VGGNet
gptkb:Res_Ne_Xt
gptkb:Google_Net
|
|
gptkbp:is_optimized_for
|
Parameter Efficiency
|
|
gptkbp:is_part_of
|
Deep Learning Frameworks
|
|
gptkbp:is_popular_in
|
Deep Learning Community
|
|
gptkbp:is_recognized_by
|
Academic Conferences
Industry Applications
Kaggle Competitions
|
|
gptkbp:is_related_to
|
gptkb:stage_adaptation
gptkb:Hyperparameter_Tuning
Data Augmentation
Feature Extraction
Model Compression
|
|
gptkbp:is_supported_by
|
Research Papers
Community Contributions
Open Source Code
|
|
gptkbp:is_trained_in
|
gptkb:Adam_Optimizer
Stochastic Gradient Descent
|
|
gptkbp:is_used_in
|
Computer Vision Tasks
|
|
gptkbp:key_feature
|
Dense Connectivity
|
|
gptkbp:primary_use
|
Image Classification
|
|
gptkbp:uses
|
gptkb:Batch_Normalization
Global Average Pooling
Re LU Activation Function
|
|
gptkbp:year_established
|
gptkb:2017
|
|
gptkbp:bfsParent
|
gptkb:Transformers
gptkb:neural_networks
|
|
gptkbp:bfsLayer
|
4
|