Various approaches to quantify uncertainty in computer vision problems
Uncertainty quantification in computer vision is crucial for ensuring vision-based systems’ reliability and safety, especially when deployed in critical applications. Over the years, various approaches have been developed to address and quantify this uncertainty. Here’s a look at some of the most prominent methods:
- Bayesian Neural Networks (BNNs): These neural networks treat weights as probability distributions rather than fixed values. By doing so, they can provide a measure of uncertainty for their predictions. During inference, multiple forward passes are made with different weight samples, producing a distribution of outputs that capture the model’s uncertainty.
- Monte Carlo dropout: Monte Carlo dropout involves performing dropout during inference. By running the network multiple times with dropout and averaging the results, a distribution over the outputs is obtained, which can...
