How can NN be thought of as p(x|z) in VAEs? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30 pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsOnline Variational AutoencoderTips and tricks for designing time-series variational autoencodersHow do I provide input and output for such a network structure in kerasUnsupervised Anomaly Detection in Imageswhy is mse training drastically different from the begining of each training with Encoder-DecoderTraining of Region Proposal Network (RPN)Keras: Softmax output into embedding layerKeras input dimensions for a MLPEncoder Decoder networks with varying image sizesHow to build this simple generative model?
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How can NN be thought of as p(x|z) in VAEs?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30 pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsOnline Variational AutoencoderTips and tricks for designing time-series variational autoencodersHow do I provide input and output for such a network structure in kerasUnsupervised Anomaly Detection in Imageswhy is mse training drastically different from the begining of each training with Encoder-DecoderTraining of Region Proposal Network (RPN)Keras: Softmax output into embedding layerKeras input dimensions for a MLPEncoder Decoder networks with varying image sizesHow to build this simple generative model?
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So, you can feed random input (z) to a NN and make the output random, but that will be just f(p(z)), where f(.) is the deterministic NN. The other way is to think of the NN output as parameteres of a certain distribution and as such, the NN is P(x|z). BUT, that's not happening, the decoder of a VAE is producing an image (x), and it is not random! so, for a given z, it will produce the same x, all the time. Why do they think of it as P(x|z) in the derivations of VAEs??
machine-learning deep-learning keras
New contributor
$endgroup$
add a comment |
$begingroup$
So, you can feed random input (z) to a NN and make the output random, but that will be just f(p(z)), where f(.) is the deterministic NN. The other way is to think of the NN output as parameteres of a certain distribution and as such, the NN is P(x|z). BUT, that's not happening, the decoder of a VAE is producing an image (x), and it is not random! so, for a given z, it will produce the same x, all the time. Why do they think of it as P(x|z) in the derivations of VAEs??
machine-learning deep-learning keras
New contributor
$endgroup$
add a comment |
$begingroup$
So, you can feed random input (z) to a NN and make the output random, but that will be just f(p(z)), where f(.) is the deterministic NN. The other way is to think of the NN output as parameteres of a certain distribution and as such, the NN is P(x|z). BUT, that's not happening, the decoder of a VAE is producing an image (x), and it is not random! so, for a given z, it will produce the same x, all the time. Why do they think of it as P(x|z) in the derivations of VAEs??
machine-learning deep-learning keras
New contributor
$endgroup$
So, you can feed random input (z) to a NN and make the output random, but that will be just f(p(z)), where f(.) is the deterministic NN. The other way is to think of the NN output as parameteres of a certain distribution and as such, the NN is P(x|z). BUT, that's not happening, the decoder of a VAE is producing an image (x), and it is not random! so, for a given z, it will produce the same x, all the time. Why do they think of it as P(x|z) in the derivations of VAEs??
machine-learning deep-learning keras
machine-learning deep-learning keras
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Alex DeftAlex Deft
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