You can leverage unsupervised learning in VAE to improve latent representation in image generation, here is the code snippet you can refer to:
- Reconstruction Loss: Use unsupervised reconstruction loss (e.g., MSE or BCE) to encourage the decoder to accurately recreate input images.
- KL Divergence: Regularize the latent space using KL divergence to enforce a smooth, continuous distribution.
- Latent Space Clustering: Use clustering techniques (e.g., k-means) on the latent space to group similar representations for better disentanglement.
- Augmented Data: Employ data augmentation to improve the diversity and robustness of latent representations.
Here is the code snippet you can refer to:
In the above code we are using the following key points:
- KL Divergence Regularization: Ensures a structured and smooth latent space.
- Reconstruction Loss: Encourages faithful reconstruction of input data.
- Latent Space Analysis: Employ clustering or disentanglement techniques post-training.
- Data Augmentation: Improves robustness and diversity of the learned representations.
Hence, by referring to above, you can leverage unsupervised learning in VAE to improve latent representation in image generation.
