fangleai/Variational-inference-on-MNIST

A list of variational inference algorithms and their performance on MNIST

Variational-inference-on-MNIST

A list of variational inference algorithms and their performance on MNIST if applicable.

The common setting of doing variational inference is as following: given a set of data points x, we assume latent varable z and aim to recover the true poster p(z|x), which is analytically intractable. We take an approximation distribution q(z) or q(z|x) from an tractable distribution family and let it approach the true posterior as "close" as possible. MNIST dataset is composed of pictures features with handwritten digits, which is a common benchmark dataset. The performance of variational inference algorithms on MNIST are usually measured in terms of the maximum data-log likelihood they can achieve and the "quality" of their reconstruction pictures.

This page aims to list up-to-date variational inference algorithms and their performance on MNIST in roughly time order. Contributions and comments are more than welcome. "NLL" denotes "negative log likelihood".

• SBAI - Stochastic Backpropagation and Approximate Inference in Deep Generative Models ICML 2014

• VAE - Auto-Encoding Variational Bayes ICLR 2014

• VINF - Variational Inference with Normalizing Flows ICML 2015, DLGM+NF 85.1, DLGM+NICE 87.2, DLGM + HVI 85.51, DARN 84.13

• Review - Markov Chain Monte Carlo and Variational Inference: Bridging the Gap, ICML 2015

• Importance weighted VAE - Importance Weighted Autoencoders, ICLR2016, VAE 84.78, IWAE 82.90

• A-DGM - Auxiliary Deep Generative Models ICML 2016

• IAF - Improved Variational Inference with Inverse Autoregressive Flow, NIPS 2016, Convolutional VAE + HVI 81.94, DLGM 2hl + IWAE 82.90, LVAE 81.74, IAF 79.10

• Review paper - On the Quantitative Analysis of Decoder-Based Generative Models ICLR 2017, AIS 85.679, AIS+Encoder 85.754, IWAE 86.902

• AVB - Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks, ICML 2017, AVB+AC 80.2, VAE 81.9, VAE+IAF 79.1

• Alice - ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching NIPS 2017

• VAE-SVGD - VAE Learning via Stein Variational Gradient Descent NIPS 2017, Stein VIWAE 82.88

• SIVI - Semi-Implicit Variational Inference, ICML 2018

• KIVI - Kernel Implicit Variational Inference, ICLR 2018

• CTF - Continuous-Time Flows for Efficient Inference and Density Estimation ICML 2018

• CVB - Coupled Variational Bayes via Optimization Embedding, NIPS 2018, CVB 84.0

• UIVI - Unbiased Implicit Variational Inference, Submitted AISTATS 2019, VAE 98.29, SIVI, 97.77, UIVI 94.09

• DDNF - Deep Diffeomorphic Normalizing Flows

• MD-GAN - Mixture Density Generative Adversarial Networks

• VI-ID - Variational Inference using Implicit Distributions

• DSIVI - Doubly Semi-Implicit Variational Inference