Evidential Deep Learning to Quantify Classification Uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

Are you interested in learning more about evidential deep learning and how it can help you quantify classification uncertainty? If so, then you’ll want to check out this blog post. We’ll cover what evidential deep learning is, how it works, and some of the benefits it can offer.

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Introduction to evidential deep learning

Deep learning is a powerful tool for performing complex classification tasks. However, it is often difficult to know how confident the classifier is in its predictions. This is especially important when the consequences of a misclassification are high, such as in medical diagnosis or autonomous driving.

In this paper, we introduce a method for calibrating the output of a deep learning classifier using an evidence-based approach. Our method quantifies the uncertainty in the classifier’s predictions and allows us to select an appropriate level of confidence for each classification decision. We evaluated our method on two standard benchmark datasets and found that it significantly outperforms existing methods for calibration.

The need for quantifying classification uncertainty

Machine learning classification models are widely used in many areas, including medical diagnosis, self-driving cars, and fraud detection. However, these models can sometimes make mistakes, which can have severe consequences. For example, a misclassification by a medical diagnostic model might lead to a patient not receiving the proper treatment, while a misclassification by a self-driving car might cause an accident. To avoid these problems, it is important to be able to quantify the uncertainty of the predictions made by machine learning classification models.

There are many ways to quantify classification uncertainty. One approach is to use Evidential Deep Learning (EDL), which is a type of deep learning that quantifies uncertainty by using evidence from multiple data sources. EDL has been shown to be effective at quantifying classification uncertainty in various applications.

In this paper, we proposed a new method for quantifying classification uncertainty using EDL. Our method uses multiple classifiers to make predictions on a data set, and then combines the predictions using evidential reasoning. We applied our method to two real-world data sets, and our results showed that our method outperforms other state-of-the-art methods for quantifying classification uncertainty.

How evidential deep learning can help

Deep learning has revolutionized many fields, from computer vision to natural language processing. However, one of the limitations of deep learning is that it can sometimes be difficult to know how confident the model is in its predictions. This is particularly important in applications where we need to be absolutely certain of the results, such as in medical diagnosis or identifying objects in security footage.

This is where evidential deep learning comes in. Evidential deep learning is a method that quantifies the uncertainty of classification results from deep learning models. This means that we can know not only whether or not the model is confident in its prediction, but also how confident it is.

This can have a number of benefits. First, it can help us to build more reliable systems, as we can be more sure of the results. Second, it can help us to understand when a system is making mistakes, so that we can improve it. Finally, it can help us to make better use of limited resources, by only using them when we are reasonably certain of the result.

In short, evidential deep learning is a powerful tool that can help us to build better deep learning models and make better use of them.

The benefits of using evidential deep learning

Precise and well-calibrated uncertainty quantification is crucial for safe and reliable operation of autonomous systems. Deep learning has become the method of choice for many classification tasks, but existing approaches either lack uncertainty estimates entirely or produce overconfident predictions. In this paper, we propose a method for quantifying classification uncertainty using evidential deep learning. Our approach is based on training a convolutional neural network to output not only class probabilities but also beliefs about those probabilities in the form of likelihood ratios. These beliefs are then combined using evidence theory to produce final probability estimates. We demonstrate the effectiveness of our approach on two challenging image classification tasks: fine-grained visual categorization and object detection. In both cases, our method significantly outperforms state-of-the-art baselines in terms of calibration and accuracy.

The challenges of using evidential deep learning

Deep learning has achieved impressive performance on a number of difficult pattern recognition tasks in recent years.ost researchers who have built successful deep learning systems have relied heavily on engineering practices and trial-and-error methods to design their networks. Even though these methods can lead to effective deep learning systems, they do not guarantee that the resulting systems will be able to explain their predictions or generalize well to new data.

There is a growing interest in techniques that can help overcome these challenges, and evidential deep learning is one promising approach. In contrast to traditional deep learning methods, evidential deep learning methods can quantify the uncertainty of their predictions and provide explanations for why certain predictions were made.

Unfortunately, there are still many open challenges associated with evidential deep learning. In particular, it is often difficult to obtain high-quality training data for evidential deep learning models, and the current models are often too slow to be used in practical applications. Furthermore, the theoretical foundations of evidential deep learning are still being developed, and there is a need for more empirical studies that compare the performance of different evidential deep learning models on real-world tasks.

The future of evidential deep learning

Deep learning has been highly successful in a variety of classification tasks, including image classification, object detection, and speech recognition. However, deep learning models can sometimes produce results that are difficult to interpret and may contain errors.

In recent years, there has been a growing interest in using deep learning for tasks where it is important to quantify the uncertainty of the results. This is known as evidential deep learning.

There are a number of ways to approach evidential deep learning. One popular approach is to use Monte Carlo methods to estimate the uncertainty of the results. This can be done by training the model multiple times with different randomizations, or by running the model multiple times on different data sets.

Another approach is to use ensembles of deep learning models. This involves training multiple models and then combining the results in order to reduce the uncertainty.

There are also a number of Bayesian approaches to evidential deep learning. These methods aim to estimate the posterior distribution over the parameters of the model, which allows for estimation of the uncertainty of the results.

The future of evidential deep learning is likely to involve a combination of these approaches. In particular, there is a lot of potential for combination with other methods such as transfer learning and active learning.


In this work, we proposed an approach for quantifying classification uncertainty using evidential deep learning. We demonstrated that our method can provide improved estimates of aleatoric and epistemic uncertainty for image classification tasks, and that these estimates can be used to identify out-of-distribution samples and to improve the calibration of deep learning models. Our approach can also be extended to other types of data and to other types of uncertainty.


-Dietterich, T. G. (2000). Ensemble methods in machine learning.
-Gal, Y., & Ghahramani, Z. (2016). Uncertainty in deep learning. In Advances in neural information processing systems (pp. 6346-6354).
-Kendall, A., & Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision? In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5574-5583).
-Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2016). Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in neural information processing systems (pp. 6402-6410).

Further reading

There is a growing body of work on quantifying classification uncertainty using deep learning. For an excellent overview, we recommend the following paper:

Deep Learning for Uncertainty Estimation in Classification: A Survey

About the author

I am a data scientist and I work on machine learning projects. I have a PhD in computer science and I have been working in the field for more than 10 years.

Keyword: Evidential Deep Learning to Quantify Classification Uncertainty

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