Unofficial LIDVAE[1] implementation.
Rewritten in PyTorch for basic image generation tasks.
For the official TensorFlow implementation, see here.
The following features are also implemented:
-
$\beta$ from$\beta$ -VAE[2] -
$\log{\text{MSE}}$ which means optimal decoder variance from$\sigma$ -VAE[3] - Inverse lipschitz constraints from IL-LIDVAE[4]
[1] Wang, Yixin, David Blei, and John P. Cunningham. "Posterior collapse and latent variable non-identifiability." Advances in Neural Information Processing Systems 34 (2021): 5443-5455.
[2] Higgins, Irina, et al. "beta-vae: Learning basic visual concepts with a constrained variational framework." ICLR (Poster) 3 (2017).
[3] Rybkin, Oleh, Kostas Daniilidis, and Sergey Levine. "Simple and effective vae training with calibrated decoders." International conference on machine learning. PMLR, 2021.
[4] Kinoshita, Yuri, et al. "Controlling posterior collapse by an inverse Lipschitz constraint on the decoder network." International Conference on Machine Learning. PMLR, 2023.