tilman151 / ae_bakeoff Goto Github PK

You posted on Reddit.

I think this is very cool.

In the Reddit post you ask if you missed any AE types, but in the blog post you encourage people to add PRs to add AE types. I'm too new to do the latter, so I'm just going to mention the names I know.

Your existing types:

• Shallow AE
• Deep (vanilla) AE
• Stacked AE
• Sparse AE
• Denoising AE
• VAE
• beta-VAE
• vq-VAE

I'm curious to see if vqVAE2 does better than 1 in here.

This list doesn't have

• TD-VAE,
• Contractive AE,
• Undercomplete AE,
• Convolutional AE,
• Pixel VAE,
• Conditional VAE,
• DFC VAE,
• Derivative Penalized VAE,
• Stacked Similarity-Aware AE,
• Replacement AE,
• AE2-Net,
• VAE-SNE,
• Correspondence AE,
• Adversarial Conditional AE,
• Prior Flow VAE,
• Factorized Gaussian Process VAE,
• Scalable Gaussian Process VAE,
• Sparse Gaussian Process VAE,
• VAE-LSTM,
• LSTM-based VAE (actually very different,)
• SNF-CVAE,
• MAD-VAE,
• Disentangled Causal Effect VAE,
• Coupled VAE,
• Triplet Enhanced Autoencoder,
• Hypergraph VAE,
• Exemplar VAE,
• Autoencoding VAE (amusingly it's a good name,)
• Geometry-aware Hamiltonian VAE,
• Quaternion-valued VAE,
• Heirarchal Latent Embedding VQ-VAE,
• Dirichlet Graph VAE,
• Discriminative Mixture VAE,
• Complex-valued VAE,
• Variational Recurrent AE,
• NCP-VAE,
• Targeted VAE,
• Heirarchical Sparse VAE,
• Discrete Memory Addressing VAE,
• Multi-adversarial VAE,
• VAE-BRIDGE,
• Cycle VAE,
• Dynamic VAE,
• DCAVN,
• Ordinal Content VAE,
• Quasi-symplectic Langevin VAE,
• Stochastic Recurrent Networks,
• Deep Kalman Filters,
• Kalman VAEs,
• Stochastic Recurrent NNs,
• Disentangled Sequential AEs,
• Neural Adaptive Sequential Monte Carlo

and arguably

• Variational Converter-Encoder

plus things that don't apply, except maybe they do because gpt-3 makes images, like

• Plan CVAE,
• Condition-Transforming VAE,
• Disentangled Causal Representation Learning