What do we visualize in showing a VAE latent space? The 2019 Stack Overflow Developer Survey Results Are InKeras VAE example loss functionWhat mu and sigma vector really mean in VAE?Latent Space of VAEWhat is the mathematical definition of the latent spaceKL divergence in VAEAny heuristic for minimal DCGAN latent space dimension?Variational auto-encoders (VAE): why the random sample?Understanding ELBO Learning Dynamics for VAE?What is “posterior collapse” phenomenon?InvalidArgumentError: incompatible shapes: [32,153] vs [32,5] , when using VAE

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What do we visualize in showing a VAE latent space?



The 2019 Stack Overflow Developer Survey Results Are InKeras VAE example loss functionWhat mu and sigma vector really mean in VAE?Latent Space of VAEWhat is the mathematical definition of the latent spaceKL divergence in VAEAny heuristic for minimal DCGAN latent space dimension?Variational auto-encoders (VAE): why the random sample?Understanding ELBO Learning Dynamics for VAE?What is “posterior collapse” phenomenon?InvalidArgumentError: incompatible shapes: [32,153] vs [32,5] , when using VAE










2












$begingroup$


I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go from $N$ input dimensions to $H$ hidden dimensions to a $Z$ dimensional Gaussian with $Z$ mean values, and $Z$ variance values. For example here (which is based off the official PyTorch VAE example), $N=784, H=400$ and $Z=20$.



When people make 2D scatter plots what do they actually plot? In the above example the bottleneck layer is 20 dimensional, which means there are 40 features (counting both $mu$ and $sigma$). Do people do PCA or tSNE or something on this? Even if $Z=2$ there is still four features so I don't understand how the scatter plot showing clustering, say in MNIST, is being made.










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    2












    $begingroup$


    I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go from $N$ input dimensions to $H$ hidden dimensions to a $Z$ dimensional Gaussian with $Z$ mean values, and $Z$ variance values. For example here (which is based off the official PyTorch VAE example), $N=784, H=400$ and $Z=20$.



    When people make 2D scatter plots what do they actually plot? In the above example the bottleneck layer is 20 dimensional, which means there are 40 features (counting both $mu$ and $sigma$). Do people do PCA or tSNE or something on this? Even if $Z=2$ there is still four features so I don't understand how the scatter plot showing clustering, say in MNIST, is being made.










    share|improve this question







    New contributor




    ITA is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      2












      2








      2





      $begingroup$


      I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go from $N$ input dimensions to $H$ hidden dimensions to a $Z$ dimensional Gaussian with $Z$ mean values, and $Z$ variance values. For example here (which is based off the official PyTorch VAE example), $N=784, H=400$ and $Z=20$.



      When people make 2D scatter plots what do they actually plot? In the above example the bottleneck layer is 20 dimensional, which means there are 40 features (counting both $mu$ and $sigma$). Do people do PCA or tSNE or something on this? Even if $Z=2$ there is still four features so I don't understand how the scatter plot showing clustering, say in MNIST, is being made.










      share|improve this question







      New contributor




      ITA is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go from $N$ input dimensions to $H$ hidden dimensions to a $Z$ dimensional Gaussian with $Z$ mean values, and $Z$ variance values. For example here (which is based off the official PyTorch VAE example), $N=784, H=400$ and $Z=20$.



      When people make 2D scatter plots what do they actually plot? In the above example the bottleneck layer is 20 dimensional, which means there are 40 features (counting both $mu$ and $sigma$). Do people do PCA or tSNE or something on this? Even if $Z=2$ there is still four features so I don't understand how the scatter plot showing clustering, say in MNIST, is being made.







      machine-learning autoencoder vae






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          1 Answer
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          $begingroup$


          When people make 2D scatter plots what do they actually plot?




          First case: when we want to get an embedding for specific inputs:



          We either



          1. Feed a hand-written character "9" to VAE, receive a 20 dimensional "mean" vector, then embed it into 2D dimension using t-SNE, and finally plot it with label "9" or the actual image next to the point, or


          2. We use 2D mean vectors and plot directly without using t-SNE.


          Note that "variance" vector is not used for embedding. However, its size can be used to show the degree of uncertainty. For example a clear "9" would have less variance than a hastily written "9" which is close to "0".



          Second case: when we want to plot a random sample of z space:




          1. We select random values of z, which effectively bypasses sampling from mean and variance vectors,



            sample = Variable(torch.randn(64, ZDIMS))



          2. Then, we feed those z's to decoder, and receive images,



            sample = model.decode(sample).cpu()


          3. Finally, we embed z's into 2D dimension using t-SNE, or use 2D dimension for z and plot directly.


          Here is an illustration for the second case (drawn by the one and only paint):



          enter image description here



          As you see, the mean and variances are completely bypassed, we directly give the random z's to decoder.



          The referenced article says the same thing, but less obvious:




          Below you see 64 random samples of a two-dimensional latent space of MNIST digits that I made with the example below, with ZDIMS=2




          and




          VAE has learned a 20-dimensional normal distribution for any input digit




          ZDIMS = 20
          ...
          self.fc21 = nn.Linear(400, ZDIMS) # mu layer
          self.fc22 = nn.Linear(400, ZDIMS) # logvariance layer


          which means it only refers to the z vector, bypassing mean and variance vectors.






          share|improve this answer











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            1 Answer
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            active

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            active

            oldest

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            1












            $begingroup$


            When people make 2D scatter plots what do they actually plot?




            First case: when we want to get an embedding for specific inputs:



            We either



            1. Feed a hand-written character "9" to VAE, receive a 20 dimensional "mean" vector, then embed it into 2D dimension using t-SNE, and finally plot it with label "9" or the actual image next to the point, or


            2. We use 2D mean vectors and plot directly without using t-SNE.


            Note that "variance" vector is not used for embedding. However, its size can be used to show the degree of uncertainty. For example a clear "9" would have less variance than a hastily written "9" which is close to "0".



            Second case: when we want to plot a random sample of z space:




            1. We select random values of z, which effectively bypasses sampling from mean and variance vectors,



              sample = Variable(torch.randn(64, ZDIMS))



            2. Then, we feed those z's to decoder, and receive images,



              sample = model.decode(sample).cpu()


            3. Finally, we embed z's into 2D dimension using t-SNE, or use 2D dimension for z and plot directly.


            Here is an illustration for the second case (drawn by the one and only paint):



            enter image description here



            As you see, the mean and variances are completely bypassed, we directly give the random z's to decoder.



            The referenced article says the same thing, but less obvious:




            Below you see 64 random samples of a two-dimensional latent space of MNIST digits that I made with the example below, with ZDIMS=2




            and




            VAE has learned a 20-dimensional normal distribution for any input digit




            ZDIMS = 20
            ...
            self.fc21 = nn.Linear(400, ZDIMS) # mu layer
            self.fc22 = nn.Linear(400, ZDIMS) # logvariance layer


            which means it only refers to the z vector, bypassing mean and variance vectors.






            share|improve this answer











            $endgroup$

















              1












              $begingroup$


              When people make 2D scatter plots what do they actually plot?




              First case: when we want to get an embedding for specific inputs:



              We either



              1. Feed a hand-written character "9" to VAE, receive a 20 dimensional "mean" vector, then embed it into 2D dimension using t-SNE, and finally plot it with label "9" or the actual image next to the point, or


              2. We use 2D mean vectors and plot directly without using t-SNE.


              Note that "variance" vector is not used for embedding. However, its size can be used to show the degree of uncertainty. For example a clear "9" would have less variance than a hastily written "9" which is close to "0".



              Second case: when we want to plot a random sample of z space:




              1. We select random values of z, which effectively bypasses sampling from mean and variance vectors,



                sample = Variable(torch.randn(64, ZDIMS))



              2. Then, we feed those z's to decoder, and receive images,



                sample = model.decode(sample).cpu()


              3. Finally, we embed z's into 2D dimension using t-SNE, or use 2D dimension for z and plot directly.


              Here is an illustration for the second case (drawn by the one and only paint):



              enter image description here



              As you see, the mean and variances are completely bypassed, we directly give the random z's to decoder.



              The referenced article says the same thing, but less obvious:




              Below you see 64 random samples of a two-dimensional latent space of MNIST digits that I made with the example below, with ZDIMS=2




              and




              VAE has learned a 20-dimensional normal distribution for any input digit




              ZDIMS = 20
              ...
              self.fc21 = nn.Linear(400, ZDIMS) # mu layer
              self.fc22 = nn.Linear(400, ZDIMS) # logvariance layer


              which means it only refers to the z vector, bypassing mean and variance vectors.






              share|improve this answer











              $endgroup$















                1












                1








                1





                $begingroup$


                When people make 2D scatter plots what do they actually plot?




                First case: when we want to get an embedding for specific inputs:



                We either



                1. Feed a hand-written character "9" to VAE, receive a 20 dimensional "mean" vector, then embed it into 2D dimension using t-SNE, and finally plot it with label "9" or the actual image next to the point, or


                2. We use 2D mean vectors and plot directly without using t-SNE.


                Note that "variance" vector is not used for embedding. However, its size can be used to show the degree of uncertainty. For example a clear "9" would have less variance than a hastily written "9" which is close to "0".



                Second case: when we want to plot a random sample of z space:




                1. We select random values of z, which effectively bypasses sampling from mean and variance vectors,



                  sample = Variable(torch.randn(64, ZDIMS))



                2. Then, we feed those z's to decoder, and receive images,



                  sample = model.decode(sample).cpu()


                3. Finally, we embed z's into 2D dimension using t-SNE, or use 2D dimension for z and plot directly.


                Here is an illustration for the second case (drawn by the one and only paint):



                enter image description here



                As you see, the mean and variances are completely bypassed, we directly give the random z's to decoder.



                The referenced article says the same thing, but less obvious:




                Below you see 64 random samples of a two-dimensional latent space of MNIST digits that I made with the example below, with ZDIMS=2




                and




                VAE has learned a 20-dimensional normal distribution for any input digit




                ZDIMS = 20
                ...
                self.fc21 = nn.Linear(400, ZDIMS) # mu layer
                self.fc22 = nn.Linear(400, ZDIMS) # logvariance layer


                which means it only refers to the z vector, bypassing mean and variance vectors.






                share|improve this answer











                $endgroup$




                When people make 2D scatter plots what do they actually plot?




                First case: when we want to get an embedding for specific inputs:



                We either



                1. Feed a hand-written character "9" to VAE, receive a 20 dimensional "mean" vector, then embed it into 2D dimension using t-SNE, and finally plot it with label "9" or the actual image next to the point, or


                2. We use 2D mean vectors and plot directly without using t-SNE.


                Note that "variance" vector is not used for embedding. However, its size can be used to show the degree of uncertainty. For example a clear "9" would have less variance than a hastily written "9" which is close to "0".



                Second case: when we want to plot a random sample of z space:




                1. We select random values of z, which effectively bypasses sampling from mean and variance vectors,



                  sample = Variable(torch.randn(64, ZDIMS))



                2. Then, we feed those z's to decoder, and receive images,



                  sample = model.decode(sample).cpu()


                3. Finally, we embed z's into 2D dimension using t-SNE, or use 2D dimension for z and plot directly.


                Here is an illustration for the second case (drawn by the one and only paint):



                enter image description here



                As you see, the mean and variances are completely bypassed, we directly give the random z's to decoder.



                The referenced article says the same thing, but less obvious:




                Below you see 64 random samples of a two-dimensional latent space of MNIST digits that I made with the example below, with ZDIMS=2




                and




                VAE has learned a 20-dimensional normal distribution for any input digit




                ZDIMS = 20
                ...
                self.fc21 = nn.Linear(400, ZDIMS) # mu layer
                self.fc22 = nn.Linear(400, ZDIMS) # logvariance layer


                which means it only refers to the z vector, bypassing mean and variance vectors.







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited 5 hours ago

























                answered 8 hours ago









                EsmailianEsmailian

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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result