Towards neural networks that provably know when they don*t know
It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don*t know. However, this is a highly important property in safety critical applications. In the context of out-of-distribution detection (OOD) there have been a number of proposals to mitigate this problem but none of them are able to make any mathematical guarantees. In this paper we propose a new approach to OOD which overcomes both problems. Our approach can be used with ReLU networks and provides provably low confidence predictions far away from the training data as well as the first certificates for low confidence predictions in a neighborhood of an out-distribution point. In the experiments we show that state-of-the-art methods fail in this worst-case setting whereas our model can guarantee its performance while retaining state-of-the-art OOD performance.
The authors present a novel approach for OOD detection; in particular their approach comes with worst-case guarantees without compromising on performance. The manuscript is clearly written and I only have some concerns regarding the evaluation. First, while the authors include with MCD an uncertainty-aware training approach, I miss more state-of-the-art methods with substantially better OOD performance, including Evidential Deep Learning (Sensoy et al, NeurIPS 2018) and Deep Ensembles. In particular a comparison to EDL would be interesting, since a similar entropy-encouraging term in the loss function is used during training, resulting is maximum entropy for OOD samples. Second, I would have liked to see precision and recall of the OOD detection task in addition to AUC, allowing a more meaningful/complete comparison between the approaches.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Towards neural networks that provably know when they don*t know
It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don*t know. However, this is a highly important property in safety critical applications. In the context of out-of-distribution detection (OOD) there have been a number of proposals to mitigate this problem but none of them are able to make any mathematical guarantees. In this paper we propose a new approach to OOD which overcomes both problems. Our approach can be used with ReLU networks and provides provably low confidence predictions far away from the training data as well as the first certificates for low confidence predictions in a neighborhood of an out-distribution point. In the experiments we show that state-of-the-art methods fail in this worst-case setting whereas our model can guarantee its performance while retaining state-of-the-art OOD performance.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors present a novel approach for OOD detection; in particular their approach comes with worst-case guarantees without compromising on performance. The manuscript is clearly written and I only have some concerns regarding the evaluation. First, while the authors include with MCD an uncertainty-aware training approach, I miss more state-of-the-art methods with substantially better OOD performance, including Evidential Deep Learning (Sensoy et al, NeurIPS 2018) and Deep Ensembles. In particular a comparison to EDL would be interesting, since a similar entropy-encouraging term in the loss function is used during training, resulting is maximum entropy for OOD samples. Second, I would have liked to see precision and recall of the OOD detection task in addition to AUC, allowing a more meaningful/complete comparison between the approaches.
assistant
The authors present a novel approach for OOD detection; in particular their approach comes with worst-case guarantees without compromising on performance. The manuscript is clearly written and I only have some concerns regarding the evaluation. First, while the authors include with MCD an uncertainty-aware training approach, I miss more state-of-the-art methods with substantially better OOD performance, including Evidential Deep Learning (Sensoy et al, NeurIPS 2018) and Deep Ensembles. In particular a comparison to EDL would be interesting, since a similar entropy-encouraging term in the loss function is used during training, resulting is maximum entropy for OOD samples. Second, I would have liked to see precision and recall of the OOD detection task in addition to AUC, allowing a more meaningful/complete comparison between the approaches.