Evidential Deep Learning to Quantify Classification Uncertainty
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems. However, as the standard approach is to train the network to minimize a prediction loss, the resultant model remains ignorant to its prediction confidence. Orthogonally to Bayesian neural nets that indirectly infer prediction uncertainty through weight uncertainties, we propose explicit modeling of the same using the theory of subjective logic. By placing a Dirichlet distribution on the class probabilities, we treat predictions of a neural net as subjective opinions and learn the function that collects the evidence leading to these opinions by a deterministic neural net from data. The resultant predictor for a multi-class classification problem is another Dirichlet distribution whose parameters are set by the continuous output of a neural net. We provide a preliminary analysis on how the peculiarities of our new loss function drive improved uncertainty estimation. We observe that our method achieves unprecedented success on detection of outof-distribution queries and endurance against adversarial perturbations.
- Novelty is below the NIPS level. The claim of using the DST theory was stated in the beginning (L36) as: achieving a "richer" representation. I did not see through the rest of the paper why this is a "richer" representation.   - The experiments are rigorously performed and analysed. They well serve the cause of the paper.   - The text in the introduction is trying to be too verbose for a scientific paper at this level. There is no need for that in my opinion. An example is: "All these (no longer) futuristic ... Are they futuristic or are they not? I'd go in favour of a more rigorous and unequivocal (and still eye-catching) presentation style.   - L85: 13]. "Hence, it allows one to use the principles of evidential theory to quantify belief masses and uncertainty through a well-defined theoretical framework.": Ok, but why is using the principles of evidential theory preferred? A Dirichlet can be used without resorting to this theory, i.e. in a Bayesian setting. The motivation can be a bit clearer here too (in the beginning of Section 3, I mean).  - There are some grammar mistakes and typos. Examples include:   -- L64: "The consequence is then unreliability of ..."  -- L75: "our approach proposed in this paper ..."  - L22: Who said that the "saturation" is "fast approaching"? I mean are there citations demonstrating that? Or is that just a speculation?