How can I resolve the low-resolution outputs in a VAE model

Question

With the help of code, can you tell me how I can resolve the low-resolution outputs in a VAE model?

Answer 1

To resolve low-resolution outputs in a Variational Autoencoder (VAE), you can refer to the following adjustments:

• Increase Latent Space Size: A larger latent space allows the model to capture more detailed features.
• Use Deeper Networks: Add more layers or increase the number of filters in the encoder/decoder for better feature extraction.
• Use Convolutional Layers: Convolutional VAEs (CVAE) can model spatial hierarchies, improving resolution.
• Increase Decoder Capacity: Enhance the decoder to generate higher-resolution images.
• Use Higher-Resolution Data: Train on higher-resolution datasets to encourage the model to learn finer details.

Here is the code snippet you can refer to:

In the above code, the key adjustments are as follows:

• Latent Space Size: latent_dim=256 to capture more detailed features.
• Deeper Encoder/Decoder: Convolutional layers with more filters for better feature extraction.
• Higher Resolution: Train on higher-resolution images and adjust the model’s architecture to fit the new resolution.

Hence, these adjustments should help improve the resolution of the generated outputs from a VAE model.