Uncertainty-guided Continual Learning with Bayesian Neural Networks
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters* 	extit{importance}. In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB) where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. Uncertainty is a natural way to identify 	extit{what to remember} and 	extit{what to change} as we continually learn, and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally, we show that our model does not necessarily need task information at test time, i.e.~it does not presume knowledge of which task a sample belongs to.
The authors propose a novel method for continual learning with neural networks based on a Bayesian approach. The idea consists in working with Bayesian neural networks, using the Bayes by back-prop approach in which a factorized Gaussian variational distribution is used to approximate the true posterior. To address the continual learning setting, the authors propose to multiply the learning rate of the mean parameters in the posterior approximation by the corresponding standard deviation parameter in the posterior approximation, while the learning rate for the variance parameters in the posterior approximation is not changed. The authors also consider a version of his method which freezes the mean and variance variational parameters when the signal to noise ratio is high. The proposed method is evaluated in exhaustive experiments, showing state-of-the-art results. Clarity: The paper is clearly written and easy to read. The method proposed is well described and it would be easy to reproduce. Quality: The proposed method is well justified and the experiments performed clearly illustrate the gains with respect to previous methods. Novelty: The proposed method is novel up to my knowledge. The methodological contributions do not seem very sophisticated, but the experiments show that the proposed method, despite being very simple, works very well in practice. Significance: The experiments show that the proposed method achieves state of the art results when compared with a very large number of baselines. This indicates that the proposed method will be relevant to the community. In my opinion, this work is highly significant.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Uncertainty-guided Continual Learning with Bayesian Neural Networks
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters* 	extit{importance}. In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB) where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. Uncertainty is a natural way to identify 	extit{what to remember} and 	extit{what to change} as we continually learn, and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally, we show that our model does not necessarily need task information at test time, i.e.~it does not presume knowledge of which task a sample belongs to.
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 propose a novel method for continual learning with neural networks based on a Bayesian approach. The idea consists in working with Bayesian neural networks, using the Bayes by back-prop approach in which a factorized Gaussian variational distribution is used to approximate the true posterior. To address the continual learning setting, the authors propose to multiply the learning rate of the mean parameters in the posterior approximation by the corresponding standard deviation parameter in the posterior approximation, while the learning rate for the variance parameters in the posterior approximation is not changed. The authors also consider a version of his method which freezes the mean and variance variational parameters when the signal to noise ratio is high. The proposed method is evaluated in exhaustive experiments, showing state-of-the-art results. Clarity: The paper is clearly written and easy to read. The method proposed is well described and it would be easy to reproduce. Quality: The proposed method is well justified and the experiments performed clearly illustrate the gains with respect to previous methods. Novelty: The proposed method is novel up to my knowledge. The methodological contributions do not seem very sophisticated, but the experiments show that the proposed method, despite being very simple, works very well in practice. Significance: The experiments show that the proposed method achieves state of the art results when compared with a very large number of baselines. This indicates that the proposed method will be relevant to the community. In my opinion, this work is highly significant.
assistant
The authors propose a novel method for continual learning with neural networks based on a Bayesian approach. The idea consists in working with Bayesian neural networks, using the Bayes by back-prop approach in which a factorized Gaussian variational distribution is used to approximate the true posterior. To address the continual learning setting, the authors propose to multiply the learning rate of the mean parameters in the posterior approximation by the corresponding standard deviation parameter in the posterior approximation, while the learning rate for the variance parameters in the posterior approximation is not changed. The authors also consider a version of his method which freezes the mean and variance variational parameters when the signal to noise ratio is high. The proposed method is evaluated in exhaustive experiments, showing state-of-the-art results. Clarity: The paper is clearly written and easy to read. The method proposed is well described and it would be easy to reproduce. Quality: The proposed method is well justified and the experiments performed clearly illustrate the gains with respect to previous methods. Novelty: The proposed method is novel up to my knowledge. The methodological contributions do not seem very sophisticated, but the experiments show that the proposed method, despite being very simple, works very well in practice. Significance: The experiments show that the proposed method achieves state of the art results when compared with a very large number of baselines. This indicates that the proposed method will be relevant to the community. In my opinion, this work is highly significant.