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Difference between Deep CNN and Dense CNN
What are advantages of Artificial Neural Networks over Support Vector Machines?What's the difference between convolutional and recurrent neural networks?Convolutional Deep Belief Networks (CDBN) vs. Convolutional Neural Networks (CNN)Difference between local and dense layers in CNNsWhat is the difference between Deep Learning and traditional Artificial Neural Network machine learning?What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?Are modern CNN (convolutional neural network) as DetectNet rotate invariant?Intuitive understanding of 1D, 2D, and 3D Convolutions in Convolutional Neural NetworksDeep learning versus machine learningHow to choose the window size of CNN in deep learning?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty
margin-bottom:0;
I know it might be a silly question but am kind of new to machine learning and ANN.
Is there any difference between Deep convolutional neural network and Dense Convolutional neural network?
Thanks in advance!
machine-learning neural-network deep-learning conv-neural-network
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
add a comment
|
I know it might be a silly question but am kind of new to machine learning and ANN.
Is there any difference between Deep convolutional neural network and Dense Convolutional neural network?
Thanks in advance!
machine-learning neural-network deep-learning conv-neural-network
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
I'm voting to close this question as off-topic because it is not about programming
– desertnaut
Mar 28 at 22:54
There is no dense CNN...
– desertnaut
Mar 28 at 22:54
add a comment
|
I know it might be a silly question but am kind of new to machine learning and ANN.
Is there any difference between Deep convolutional neural network and Dense Convolutional neural network?
Thanks in advance!
machine-learning neural-network deep-learning conv-neural-network
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
I know it might be a silly question but am kind of new to machine learning and ANN.
Is there any difference between Deep convolutional neural network and Dense Convolutional neural network?
Thanks in advance!
machine-learning neural-network deep-learning conv-neural-network
machine-learning neural-network deep-learning conv-neural-network
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
edited Mar 28 at 22:54
desertnaut
25.6k9 gold badges56 silver badges89 bronze badges
25.6k9 gold badges56 silver badges89 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
asked Mar 28 at 21:12
hanna Simachewhanna Simachew
63 bronze badges
63 bronze badges
I'm voting to close this question as off-topic because it is not about programming
– desertnaut
Mar 28 at 22:54
There is no dense CNN...
– desertnaut
Mar 28 at 22:54
add a comment
|
I'm voting to close this question as off-topic because it is not about programming
– desertnaut
Mar 28 at 22:54
There is no dense CNN...
– desertnaut
Mar 28 at 22:54
I'm voting to close this question as off-topic because it is not about programming
– desertnaut
Mar 28 at 22:54
I'm voting to close this question as off-topic because it is not about programming
– desertnaut
Mar 28 at 22:54
There is no dense CNN...
– desertnaut
Mar 28 at 22:54
There is no dense CNN...
– desertnaut
Mar 28 at 22:54
add a comment
|
1 Answer
1
active
oldest
votes
Dense CNN is a type of Deep CNN in which each layer is connected with another layer deeper than itself.
What does that mean ?
In normal CNN each layer is only connected to its siblings. Consider 4 layers,output from L1 is connected to only L2, output from L2 is connected only to L3, output from L3 is connected only to L4.
In a dense CNN, consider 4 layers, output from L1 is connected to L2, L3, L4, output from L2 is connected to L3, L4, output from L3 is connected to L4.
Here is a figure to illustrate it (source of the image is from this paper):
Why do we need to do this ?
Nowadays we have neural networks with 100 layers or even more. Neural networks are trained using backpropagation. In this algorithm, gradient (derivative) of the cost function is used to update the weights of each layer. With each new layer, the value of gradient diminishes, specially if you are using sigmoid. This results in longer time to train or sometimes it doesn't train at all. This problem is also known as vanishing gradient. Direct connection in Dense CNN solves this problem.
Dense CNN are also less prone to overfitting as compared to normal CNN.
For more read this paper, it's pretty easy to follow.
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
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1 Answer
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1 Answer
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active
oldest
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active
oldest
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active
oldest
votes
Dense CNN is a type of Deep CNN in which each layer is connected with another layer deeper than itself.
What does that mean ?
In normal CNN each layer is only connected to its siblings. Consider 4 layers,output from L1 is connected to only L2, output from L2 is connected only to L3, output from L3 is connected only to L4.
In a dense CNN, consider 4 layers, output from L1 is connected to L2, L3, L4, output from L2 is connected to L3, L4, output from L3 is connected to L4.
Here is a figure to illustrate it (source of the image is from this paper):
Why do we need to do this ?
Nowadays we have neural networks with 100 layers or even more. Neural networks are trained using backpropagation. In this algorithm, gradient (derivative) of the cost function is used to update the weights of each layer. With each new layer, the value of gradient diminishes, specially if you are using sigmoid. This results in longer time to train or sometimes it doesn't train at all. This problem is also known as vanishing gradient. Direct connection in Dense CNN solves this problem.
Dense CNN are also less prone to overfitting as compared to normal CNN.
For more read this paper, it's pretty easy to follow.
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
add a comment
|
Dense CNN is a type of Deep CNN in which each layer is connected with another layer deeper than itself.
What does that mean ?
In normal CNN each layer is only connected to its siblings. Consider 4 layers,output from L1 is connected to only L2, output from L2 is connected only to L3, output from L3 is connected only to L4.
In a dense CNN, consider 4 layers, output from L1 is connected to L2, L3, L4, output from L2 is connected to L3, L4, output from L3 is connected to L4.
Here is a figure to illustrate it (source of the image is from this paper):
Why do we need to do this ?
Nowadays we have neural networks with 100 layers or even more. Neural networks are trained using backpropagation. In this algorithm, gradient (derivative) of the cost function is used to update the weights of each layer. With each new layer, the value of gradient diminishes, specially if you are using sigmoid. This results in longer time to train or sometimes it doesn't train at all. This problem is also known as vanishing gradient. Direct connection in Dense CNN solves this problem.
Dense CNN are also less prone to overfitting as compared to normal CNN.
For more read this paper, it's pretty easy to follow.
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
add a comment
|
Dense CNN is a type of Deep CNN in which each layer is connected with another layer deeper than itself.
What does that mean ?
In normal CNN each layer is only connected to its siblings. Consider 4 layers,output from L1 is connected to only L2, output from L2 is connected only to L3, output from L3 is connected only to L4.
In a dense CNN, consider 4 layers, output from L1 is connected to L2, L3, L4, output from L2 is connected to L3, L4, output from L3 is connected to L4.
Here is a figure to illustrate it (source of the image is from this paper):
Why do we need to do this ?
Nowadays we have neural networks with 100 layers or even more. Neural networks are trained using backpropagation. In this algorithm, gradient (derivative) of the cost function is used to update the weights of each layer. With each new layer, the value of gradient diminishes, specially if you are using sigmoid. This results in longer time to train or sometimes it doesn't train at all. This problem is also known as vanishing gradient. Direct connection in Dense CNN solves this problem.
Dense CNN are also less prone to overfitting as compared to normal CNN.
For more read this paper, it's pretty easy to follow.
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
Dense CNN is a type of Deep CNN in which each layer is connected with another layer deeper than itself.
What does that mean ?
In normal CNN each layer is only connected to its siblings. Consider 4 layers,output from L1 is connected to only L2, output from L2 is connected only to L3, output from L3 is connected only to L4.
In a dense CNN, consider 4 layers, output from L1 is connected to L2, L3, L4, output from L2 is connected to L3, L4, output from L3 is connected to L4.
Here is a figure to illustrate it (source of the image is from this paper):
Why do we need to do this ?
Nowadays we have neural networks with 100 layers or even more. Neural networks are trained using backpropagation. In this algorithm, gradient (derivative) of the cost function is used to update the weights of each layer. With each new layer, the value of gradient diminishes, specially if you are using sigmoid. This results in longer time to train or sometimes it doesn't train at all. This problem is also known as vanishing gradient. Direct connection in Dense CNN solves this problem.
Dense CNN are also less prone to overfitting as compared to normal CNN.
For more read this paper, it's pretty easy to follow.
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
answered Mar 29 at 9:11
DrGeneralDrGeneral
9697 silver badges17 bronze badges
9697 silver badges17 bronze badges
add a comment
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I'm voting to close this question as off-topic because it is not about programming
– desertnaut
Mar 28 at 22:54
There is no dense CNN...
– desertnaut
Mar 28 at 22:54