Introducing Active Learning for CNN under the light of Variational Inference
One main concern of the deep learning community is to increase the capacity of representation of deep networks by increasing their depth. This requires to scale up the size of the training database accordingly. Indeed a major intuition lies in the fact that the depth of the network and the size of the training set are strongly correlated. However recent works tend to show that deep learning may be handled with smaller dataset as long as the training samples are carefully selected (let us mention for instance curriculum learning). In this context we introduce a scalable and efficient active learning method that can be applied to most neural networks, especially Convolutional Neural Networks (CNN). To the best of our knowledge, this paper is the first of its kind to design an active learning selection scheme based on a variational inference for neural networks. We also deduced a formulation of the posterior and prior distributions of the weights using statistical knowledge on the Maximum Likelihood Estimator. We describe our strategy to come up with our active learning criterion. We assess its consistency by checking the accuracy obtained by successive active learning steps on two benchmark datasets MNIST and USPS. We also demonstrate its scalability towards increasing training set size.
The paper proposes to perform active learning using pool selection of deep learning mini-batches using an approximation of the bayesian posterior. Several terms are in turn approximated. The Maximum Likelihood Estimation (MLE) bayesian inference approach to active learning, the various approximations, and more generally the theoretical framework is very interesting but difficult to follow. The paper is written in poor English and is sometimes a bit painful to read. Alternative Active learning strategies and techniques do not need to be described with such detail. On the other hand, the proposed approach has a lot of complex approximations which would benefit from a more detailed/structured presentation. Another dataset would be a big plus (both datasets concern gray digits and USPS and are arguably somewhat similar).
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Introducing Active Learning for CNN under the light of Variational Inference
One main concern of the deep learning community is to increase the capacity of representation of deep networks by increasing their depth. This requires to scale up the size of the training database accordingly. Indeed a major intuition lies in the fact that the depth of the network and the size of the training set are strongly correlated. However recent works tend to show that deep learning may be handled with smaller dataset as long as the training samples are carefully selected (let us mention for instance curriculum learning). In this context we introduce a scalable and efficient active learning method that can be applied to most neural networks, especially Convolutional Neural Networks (CNN). To the best of our knowledge, this paper is the first of its kind to design an active learning selection scheme based on a variational inference for neural networks. We also deduced a formulation of the posterior and prior distributions of the weights using statistical knowledge on the Maximum Likelihood Estimator. We describe our strategy to come up with our active learning criterion. We assess its consistency by checking the accuracy obtained by successive active learning steps on two benchmark datasets MNIST and USPS. We also demonstrate its scalability towards increasing training set size.
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 paper proposes to perform active learning using pool selection of deep learning mini-batches using an approximation of the bayesian posterior. Several terms are in turn approximated. The Maximum Likelihood Estimation (MLE) bayesian inference approach to active learning, the various approximations, and more generally the theoretical framework is very interesting but difficult to follow. The paper is written in poor English and is sometimes a bit painful to read. Alternative Active learning strategies and techniques do not need to be described with such detail. On the other hand, the proposed approach has a lot of complex approximations which would benefit from a more detailed/structured presentation. Another dataset would be a big plus (both datasets concern gray digits and USPS and are arguably somewhat similar).
assistant
The paper proposes to perform active learning using pool selection of deep learning mini-batches using an approximation of the bayesian posterior. Several terms are in turn approximated. The Maximum Likelihood Estimation (MLE) bayesian inference approach to active learning, the various approximations, and more generally the theoretical framework is very interesting but difficult to follow. The paper is written in poor English and is sometimes a bit painful to read. Alternative Active learning strategies and techniques do not need to be described with such detail. On the other hand, the proposed approach has a lot of complex approximations which would benefit from a more detailed/structured presentation. Another dataset would be a big plus (both datasets concern gray digits and USPS and are arguably somewhat similar).