Venue: ICLR 2018 conference

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposes a new method, called VCL, for continual learning. This method is a combination of the online variational inference for streaming environment with Monte Carlo method. The authors further propose to maintain a coreset which consists of representative data points from the past tasks. Such a coreset is used for the main aim of avoiding the catastrophic forgetting problem in continual learning. Extensive experiments shows that VCL performs very well, compared with some state-of-the-art methods. 

The authors present two ideas for continual learning in this paper: (1) Combination of online variational inference and sampling method, (2) Use of coreset to deal with the catastrophic forgetting problem. Both ideas have been investigated in Bayesian literature, while (2) has been recently investigated in continual learning. Therefore, the authors seems to be the first to investigate the effectiveness of (1) for continual learning. From extensive experiments, the authors find that the first idea results in VCL which can outperform other state-of-the-art approaches, while the second idea plays little role. 

The finding of the effectiveness of idea (1) seems to be significant. The authors did a good job when providing a clear presentation, a detailed analysis about related work, an employment to deep discriminative models and deep generative models, and a thorough investigation of empirical performance.

There are some concerns the authors should consider:
- Since the coreset plays little role in the superior performance of VCL, it might be better if the authors rephrase the title of the paper. When the coreset is empty, VCL turns out to be online variational inference [Broderich et al., 2013; Ghahramani & Attias, 2000]. Their finding of the effectiveness of online variational inference for continual learning should be reflected in the writing of the paper as well.
- It is unclear about the sensitivity of VCL with respect to the size of the coreset. The authors should investigate this aspect.
- What is the trade-off when the size of the coreset increases?

review_title: Seemingly significant finding, but the title should be rephrased

final_score: rating: 6: Marginally above acceptance threshold
review_content: Overall, the idea of this paper is simple but interesting. Via performing variational inference in a kind of online manner, one can address continual learning for deep discriminative or generative networks with considerations of model uncertainty.

The paper is written well, and literature review is sufficient. My comment is mainly about its importance for large-scale computer vision applications. The neural networks in the experiments are shallow. 

review_title: This paper introduces a varaitional continual learning framework for neural networks. 

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper describes the problem of continual learning, the non-iid nature of most real-life data and point out to the catastrophic forgetting phenomena in deep learning. The work defends the point of view that Bayesian inference is the right approach to attack this problem and address difficulties in past implementations. 

The paper is well written, the problem is described neatly in conjunction with the past work, and the proposed algorithm is supported by experiments. The work is a useful addition to the community.

My main concern focus on the validity of the proposed model in harder tasks such as the Atari experiments in Kirkpatrick et. al. (2017) or the split CIFAR experiments in Zenke et. al. (2017). Even though the experiments carried out in the paper are important, they fall short of justifying a major step in the direction of the solution for the continual learning problem.
review_title: New framework for an important problem, supported with experiments in basic cases.

Meta-review: The paper addresses the problem of continual learning and solutions based on variational inference. Updates to the paper have improved it and addresses many of the concerns raised by the reviewers during the discussion period.
Decision: Accept (Poster)