How can you visualize the latent space of an autoencoder using the Fashion-MNIST dataset?

To visualize the latent space of an autoencoder using the Fashion-MNIST dataset, encode the test images to obtain their embeddings and plot these embeddings in a 2D scatter plot. You can color the points based on their labels to see how similar items are grouped in the latent space.

To visualize the latent space of an autoencoder with the Fashion-MNIST dataset, you first pass the test images through the encoder to obtain their embeddings. These embeddings are 2D vectors representing the images in the latent space. By plotting these vectors in a scatter plot, you can visualize how the images are distributed in the latent space. To enhance the visualization, you can color each point according to the label of the corresponding image, which helps in understanding how the autoencoder has naturally grouped similar items together in the latent space. For instance, trousers might cluster in one area while shirts cluster in another, even though the model was not trained with label information.