Rethinking Semi-Supervised Learning in VAEs

06/17/2020
by   Tom Joy, et al.
21

We present an alternative approach to semi-supervision in variational autoencoders(VAEs) that incorporates labels through auxiliary variables rather than directly through the latent variables. Prior work has generally conflated the meaning of labels, i.e. the associated characteristics of interest, with the actual label values themselves-learning latent variables that directly correspond to the label values. We argue that to learn meaningful representations, semi-supervision should instead try to capture these richer characteristics and that the construction of latent variables as label values is not just unnecessary, but actively harmful. To this end, we develop a novel VAE model, the reparameterized VAE (ReVAE), which "reparameterizes" supervision through auxiliary variables and a concomitant variational objective. Through judicious structuring of mappings between latent and auxiliary variables, we show that the ReVAE can effectively learn meaningful representations of data. In particular, we demonstrate that the ReVAE is able to match, and even improve on the classification accuracy of previous approaches, but more importantly, it also allows for more effective and more general interventions to be performed. We include a demo of ReVAE at https://github.com/thwjoy/revae-demo.

READ FULL TEXT

page 6

page 7

page 8

page 16

page 17

research
03/13/2023

Using VAEs to Learn Latent Variables: Observations on Applications in cryo-EM

Variational autoencoders (VAEs) are a popular generative model used to a...
research
07/14/2017

Guiding InfoGAN with Semi-Supervision

In this paper we propose a new semi-supervised GAN architecture (ss-Info...
research
07/19/2018

Bounded Information Rate Variational Autoencoders

This paper introduces a new member of the family of Variational Autoenco...
research
05/24/2019

mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation

It has been previously observed that training Variational Recurrent Auto...
research
09/27/2021

Challenging the Semi-Supervised VAE Framework for Text Classification

Semi-Supervised Variational Autoencoders (SSVAEs) are widely used models...
research
04/01/2017

Complexity-Aware Assignment of Latent Values in Discriminative Models for Accurate Gesture Recognition

Many of the state-of-the-art algorithms for gesture recognition are base...
research
09/22/2022

Learning Interpretable Latent Dialogue Actions With Less Supervision

We present a novel architecture for explainable modeling of task-oriente...

Please sign up or login with your details

Forgot password? Click here to reset