In CycleGAN are there two different generators and two different discriminators? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsHow to use GAN for unsupervised feature extraction from images?The Gradient descent different between in Ng coursera and Michael A. Nielsen bookHow to use deep learning to add local (e.g. repairing) transformations to images?GANs and grayscale imagery colorizationCould someone explain to me how back-prop is done for the generator in a GAN?Training Accuracy stuck in KerasCombining different features as input to Neural NetworkHow to train the generator in a recurrent GAN (Keras)Are there real world applications where deep fully connected networks are better suited than ConvNetsWhat is exactly meant by neural network that can take different types of input?
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In CycleGAN are there two different generators and two different discriminators?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsHow to use GAN for unsupervised feature extraction from images?The Gradient descent different between in Ng coursera and Michael A. Nielsen bookHow to use deep learning to add local (e.g. repairing) transformations to images?GANs and grayscale imagery colorizationCould someone explain to me how back-prop is done for the generator in a GAN?Training Accuracy stuck in KerasCombining different features as input to Neural NetworkHow to train the generator in a recurrent GAN (Keras)Are there real world applications where deep fully connected networks are better suited than ConvNetsWhat is exactly meant by neural network that can take different types of input?
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I am trying to assimilate the contents of this paper. I have a confusion about how many different networks are there in the architecture of CycleGAN. To my understanding, the concept of cycle means that there is only one generator and one discriminator which changes roles (Discriminator as generator; and Generator as Discriminator) depending on what loss we want to calculate (whether Y -> X or X -> Y). However, my friend I have a confusion and we have been debating about this without a consensus. He says that there are totally four different networks. Two generators G1 and G2; and two discriminators D1 and D2. What is the truth ?
neural-network deep-learning gan
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
$endgroup$
add a comment |
$begingroup$
I am trying to assimilate the contents of this paper. I have a confusion about how many different networks are there in the architecture of CycleGAN. To my understanding, the concept of cycle means that there is only one generator and one discriminator which changes roles (Discriminator as generator; and Generator as Discriminator) depending on what loss we want to calculate (whether Y -> X or X -> Y). However, my friend I have a confusion and we have been debating about this without a consensus. He says that there are totally four different networks. Two generators G1 and G2; and two discriminators D1 and D2. What is the truth ?
neural-network deep-learning gan
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
$endgroup$
add a comment |
$begingroup$
I am trying to assimilate the contents of this paper. I have a confusion about how many different networks are there in the architecture of CycleGAN. To my understanding, the concept of cycle means that there is only one generator and one discriminator which changes roles (Discriminator as generator; and Generator as Discriminator) depending on what loss we want to calculate (whether Y -> X or X -> Y). However, my friend I have a confusion and we have been debating about this without a consensus. He says that there are totally four different networks. Two generators G1 and G2; and two discriminators D1 and D2. What is the truth ?
neural-network deep-learning gan
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
$endgroup$
I am trying to assimilate the contents of this paper. I have a confusion about how many different networks are there in the architecture of CycleGAN. To my understanding, the concept of cycle means that there is only one generator and one discriminator which changes roles (Discriminator as generator; and Generator as Discriminator) depending on what loss we want to calculate (whether Y -> X or X -> Y). However, my friend I have a confusion and we have been debating about this without a consensus. He says that there are totally four different networks. Two generators G1 and G2; and two discriminators D1 and D2. What is the truth ?
neural-network deep-learning gan
neural-network deep-learning gan
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
asked Apr 5 at 15:50
Arun KumarArun Kumar
31
31
add a comment |
add a comment |
1 Answer
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$begingroup$
There are two generators and two discriminators.
Check out the code here
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
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$begingroup$
There are two generators and two discriminators.
Check out the code here
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
$endgroup$
add a comment |
$begingroup$
There are two generators and two discriminators.
Check out the code here
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
$endgroup$
add a comment |
$begingroup$
There are two generators and two discriminators.
Check out the code here
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
$endgroup$
There are two generators and two discriminators.
Check out the code here
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
answered Apr 5 at 23:09
Armen AghajanyanArmen Aghajanyan
58928
58928
add a comment |
add a comment |
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