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Encoder and decoder with different architecture


Tensorflow Autoencoder with 0 hidden units learns somethingAutoencoder - encoder vs decoder network size?Dimension Reduction in CLDNN (tensorflow)Tensorflow Autoencoder - How To Calculate Reconstruction Error?How to create an Autoencoder where the encoder/decoder weights are mirrored (transposed)LSTM Encoder and Decoder architecture for specific case in PytorchHow to enhance the performance of Mixed Autoencoder architecture?AutoEncoders not showing the expected output in kerasIs it valid to train the autoencoder before building the encoder/decoder models?






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I am using Variational autoencoder conditioned on some labels. I tried a lot of architectures, the one working the best (lowest loss) is the one were the decoder has three layers and the encoder has one layer.



In my sense adding the condition of labels as input to the decoder, makes the problem different from a simple compression/decompression scenario, and makes the decoder do a more complexe task to reconstruct the output.



It is right to use different encoderand decoder architecture in this case?



Thanks for you help,










share|improve this question






























    0















    I am using Variational autoencoder conditioned on some labels. I tried a lot of architectures, the one working the best (lowest loss) is the one were the decoder has three layers and the encoder has one layer.



    In my sense adding the condition of labels as input to the decoder, makes the problem different from a simple compression/decompression scenario, and makes the decoder do a more complexe task to reconstruct the output.



    It is right to use different encoderand decoder architecture in this case?



    Thanks for you help,










    share|improve this question


























      0












      0








      0








      I am using Variational autoencoder conditioned on some labels. I tried a lot of architectures, the one working the best (lowest loss) is the one were the decoder has three layers and the encoder has one layer.



      In my sense adding the condition of labels as input to the decoder, makes the problem different from a simple compression/decompression scenario, and makes the decoder do a more complexe task to reconstruct the output.



      It is right to use different encoderand decoder architecture in this case?



      Thanks for you help,










      share|improve this question














      I am using Variational autoencoder conditioned on some labels. I tried a lot of architectures, the one working the best (lowest loss) is the one were the decoder has three layers and the encoder has one layer.



      In my sense adding the condition of labels as input to the decoder, makes the problem different from a simple compression/decompression scenario, and makes the decoder do a more complexe task to reconstruct the output.



      It is right to use different encoderand decoder architecture in this case?



      Thanks for you help,







      conditional autoencoder






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 27 at 23:16









      user2576975user2576975

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