Modal Uncertainty Estimation via Discrete Latent Representation

07/25/2020
by   Di Qiu, et al.
14

Many important problems in the real world don't have unique solutions. It is thus important for machine learning models to be capable of proposing different plausible solutions with meaningful probability measures. In this work we introduce such a deep learning framework that learns the one-to-many mappings between the inputs and outputs, together with faithful uncertainty measures. We call our framework modal uncertainty estimation since we model the one-to-many mappings to be generated through a set of discrete latent variables, each representing a latent mode hypothesis that explains the corresponding type of input-output relationship. The discrete nature of the latent representations thus allows us to estimate for any input the conditional probability distribution of the outputs very effectively. Both the discrete latent space and its uncertainty estimation are jointly learned during training. We motivate our use of discrete latent space through the multi-modal posterior collapse problem in current conditional generative models, then develop the theoretical background, and extensively validate our method on both synthetic and realistic tasks. Our framework demonstrates significantly more accurate uncertainty estimation than the current state-of-the-art methods, and is informative and convenient for practical use.

READ FULL TEXT

page 7

page 8

page 12

page 13

page 14

page 15

page 16

page 17

research
10/07/2020

Conditional Generative Modeling via Learning the Latent Space

Although deep learning has achieved appealing results on several machine...
research
10/17/2020

CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images

Ambiguity is inevitable in medical images, which often results in differ...
research
09/12/2018

Coordinated Heterogeneous Distributed Perception based on Latent Space Representation

We investigate a reinforcement approach for distributed sensing based on...
research
06/18/2012

Modeling Latent Variable Uncertainty for Loss-based Learning

We consider the problem of parameter estimation using weakly supervised ...
research
01/25/2019

On the Limitations of Representing Functions on Sets

Recent work on the representation of functions on sets has considered th...
research
06/16/2021

Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders

In many contexts, creating mappings for gestural interactions can form p...
research
11/25/2020

How to train your conditional GAN: An approach using geometrically structured latent manifolds

Conditional generative modeling typically requires capturing one-to-many...

Please sign up or login with your details

Forgot password? Click here to reset