The Great Autoencoder Bake Off

Last update: Jan 06, 2023

Related tags

Miscellaneous ae_bakeoff

Overview

The Great Autoencoder Bake Off

The companion repository to a post on my blog. It contains all you need to reproduce the results.

Features

Currently featured autoencoders:

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

They are evaluated on MNIST for the following tasks:

Reconstruction quality
Quality of decoded samples from the latent space (if possible)
Quality of latent space interpolation
Structure of the latent space visualized with UMAP
ROC curve for anomaly detection with the reconstruction error
Classification accuracy of a linear layer fitted on the autoencoder's features

Installation

Clone the repository and create a new conda environment with:

conda create -n ae_bakeoff python=3.7
conda activate ae_bakeoff
conda install --file requirements.txt -c pytorch -c conda-forge

Verify the installation by running the tests:

cd ./tests
export PYTHONPATH="../src"
python -m unittest

Usage

To one-click reproduce all results, call:

cd ./src
python reproduce.py --batch_size 256 [--gpu]

If you want to run any specific experiment, call:

python run.py <autoencoder_type> --batch_size 256 [--gpu] [--anomaly]

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Comments

In re: your Reddit post
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
opened by StoneCypher 3

Releases(v1.2)

v1.2(Mar 23, 2021)

I am a dummy and forgot a power of two in the calculation of the anomaly score. This resulted in invalid scores. I now use BCE as a score to be consistent with training.

Blog update will follow.
Source code(tar.gz)
Source code(zip)
v1.1(Mar 3, 2021)

This release adds the MNIST alternatives Fashion-MNIST (FMNIST) and Kuzushiji-MNIST (KMNIST) as additional datasets. They are usable via the new CLI argument --dataset. The logs directory now has a sub-directory for each dataset.

The training time of each autoencoder is now recorded and rendered as a markdown table.

All changes are reflected in the results.
Source code(tar.gz)
Source code(zip)
v1.0(Jan 28, 2021)

Source code(tar.gz)
Source code(zip)

The Great Autoencoder Bake Off

Related tags

Overview

The Great Autoencoder Bake Off

Features

Installation

Usage

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Comments

In re: your Reddit post

Releases(v1.2)

v1.2(Mar 23, 2021)

v1.1(Mar 3, 2021)

v1.0(Jan 28, 2021)

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