title: Implement CVAE for a single image
body: <p>I have a multi-dimensional, <code>hyper-spectral</code> image (<code>channels, width, height = 15, 2500, 2500</code>). I want to compress its 15 channel dimensions into 5 channels.So, the output would be (<code>channels, width, height = 5, 2500, 2500</code>). One simple way to do is to apply PCA. However, performance is not so good. Thus, I want to use Variational AutoEncoder(VAE).
  When I saw the available solution in Tensorflow or keras library, it shows an example of clustering the <code>whole images</code> using Convolutional Variational AutoEncoder(CVAE).</p>
  <p><a href="https://www.tensorflow.org/tutorials/generative/cvae" rel="nofollow noreferrer">https://www.tensorflow.org/tutorials/generative/cvae</a></p>
  <p><a href="https://keras.io/examples/generative/vae/" rel="nofollow noreferrer">https://keras.io/examples/generative/vae/</a></p>
  <p>However, I have a single image. What is the best practice to implement CVAE? Is it by generating sample images by moving window approach?</p>