Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable and task boundaries are known during training. However, if our goal is to develop an algorithm that learns as humans do, this setting is far from realistic and it is essential to develop a methodology that works in a task-free manner. Meanwhile, among several branches of continual learning, expansion-based methods have the advantage of eliminating catastrophic forgetting by allocating new resource to learn new data. In this work, we propose an expansion-based approach for task-free continual learning for the first time. Our model, named Continual Neural Dirichlet Process Mixture (CN-DPM), consists of a set of neural network experts that are in charge of a subset of the data. CN-DPM expands the number of experts in a principled way under the Bayesian nonparametric framework.
With extensive experiments, we show that our model successfully performs taskfree continual learning for both discriminative and generative tasks such as image classification and image generation.
This paper proposes Continual Neural Dirichlet Process Mixture Model (CN-DPM) to solve task-free continual learning. The core idea is to employ Dirichlet process mixture model to create novel experts in online fashion when task distributions change.  The proposed method is validated on various tasks and demonstrated to perform well compared to the other baselines.
Overall I find this paper to be well-written and the experiments are conducted thoroughly. The method is compared to proper baselines in various settings, and the paper describes detailed experimental settings and architectural choices to help readers willing to reproduce. I’ve gone through the appendix and they provide enough additional experiments to support the author’s claim.
The main algorithm itself cannot be considered to be novel. DPM or other Bayesian nonparametric models have been extensively used for the problems requiring to adapt the model size according to the change of data. Nevertheless, the application of DPM in task-free continual learning context seems to be considered as a contribution.
I have much experience in implementing Bayesian nonparametric models with parametric distributions and compared various methods to conduct the posterior inference of them. In my experience, even for the low-dimensional parametric models, the posterior inference algorithms for DPM usually suffer from local optima, and the sequential methods such as SVA depends heavily on the data processing order. According to the experimental setting presented in the paper, the algorithm goes through a single pass over the data stream, yet still able to reasonably train (deep) neural networks and identify mixture components jointly. Do you have any intuition about how this becomes feasible?
I don’t fully understand why generative modeling is required. In page 4 the authors stated that the generative model prevents catastrophic forgetting. But in my understanding, using expert-specific parameters is the part that prevents catastrophic forgetting, not the generative model itself. Learning generative model in online fashion may work well in simple structured data such as MNIST,  but I highly doubt that the generative model could be trained properly for CIFAR10 or CIFAR100, especially in online setting. My concern is that learning generative model part may even impede the discriminative learning. Could you elaborate more on this?
Another minor concern is the way the concentration parameter alpha is selected. The authors stated that they chose proper value of alpha according to the number of tasks known in advance. I think this does not make sense. Alpha should also be inferred along with other parameters, or fixed to non-informative value if the performance of the algorithm is not very sensitive to the choice of alpha.
I think it would be more helpful to show how the task-assignment p(z=k|x) is learned. For instance, the clustering accuracy according to p(z=k|x) against the ground-truth task label can be measured, or at least qualitatively show what examples were assigned to each task.