# Modal Uncertainty Estimation via Discrete Latent Representation

arXiv: https://arxiv.org/abs/2007.12858 · HTML: https://mlsanity.com/paper/2007.12858/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Ambiguous Medical Image Segmentation using Diffusion Models](https://mlsanity.com/paper/2304.04745/) (2023)
- [Stochastic Segmentation with Conditional Categorical Diffusion Models](https://mlsanity.com/paper/2303.08888/) (2023)
- [Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts](https://mlsanity.com/paper/2212.07328/) (2022)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)

## Abstract (arXiv)

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 it 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.