Published as a conference paper at ICLR 2018 LIFELONG LEARNING WITH DYNAMICALLY EXPANDABLE NETWORKS
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets under lifelong learning scenarios, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch counterparts with substantially fewer number of parameters. Further, the obtained network fine-tuned on all tasks obtained siginficantly better performance over the batch models, which shows that it can be used to estimate the optimal network structure even when all tasks are available in the first place.
The paper was clearly written and pleasant to read. I liked the use of sparsity- and group-sparsity-promoting regularizers to select connections and decide how to expand the network.
A strength of the paper is that the proposed algorithm is interesting and intuitive, even if relatively complex, as it requires chaining a sequence of sub-algorithms. It was good to see the impact of each sub-algorithm studied separately (to some degree) in the experimental section. The results are overall strong.
It’s hard for me to judge the novelty of the approach though, as I’m not an expert on this topic.
Just a few points below:
- The experiments focus on a relevant continual learning problem, where each new task corresponds to learning a new class. In this setup, the method consistently outperforms EWC (e.g., Fig. 3), as well as the progressive network baseline.
Did the authors also check the performance on the permuted MNIST benchmark, as studied by Kirkpatrick et al. and Zenke et al.? It would be important to see how the method fares in this setting, where the tasks are the same, but the inputs have to be remapped, and network expansion is less of an issue.
- Fig. 4 would be clearer if the authors showed also the performance and how much the selected connection subsets would change if instead of using the last layer lasso + BFS, the full L1-penalized problem was solved, while keeping the rest of the pipeline intact.
- Still regarding the proposed selective retraining, the special role played by the last hidden layer seems slightly arbitrary. It may well be that it has the highest task-specificity, though this is not trivial to me. This special role might become problematic when dealing with deeper networks.