Selfless Sequential Learning
Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations, our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets.
 [REVISION] The work is thorough and some of my minor concerns have been addressed, so I am increasing my score to 6. I cannot go beyond because of the incremental nature of the work, and the very limited applicability of the used continual learning setup from this paper. [OLD REVIEW] The paper proposes a novel, regularization based, approach to the sequential learning problem using a fixed size model. The main idea is to add extra terms to the loss encouraging representation sparsity and combating catastrophic forgetting. The approach fairs well compared to other regularization based approaches on MNIST and CIFAR-100 sequential learning variants. Pros: Thorough experiments, competitive baselines and informative ablation study. Good performance on par or superior to baselines. Clear paper, well written. Cons: The approach, while competitive in performance, does not seem to fix any significant issues with baseline methods. For example, task boundaries are still used, which limits applicability; in many scenarios which do have a continual learning problem there are no clear task boundaries, such as data distribution drift in both supervised and reinforcement learning. Since models used in the work are very different from SOTA models on those particular tasks, it is hard to determine from the paper how the proposed method influences these models. In particular, it is not clear whether these changes to the loss would still allow top performance on regular classification tasks, e.g. CIFAR-10 or MNIST even without sequential learning, or in multitask learning settings. Summary: Although the work is substantial and experiments are thorough, I have reservations about extrapolating from the results to settings which do have a continual learning problem. Although I am convinced results are slightly superior to baselines, and I appreciate the lengthy amount of work which went into proving that, the paper does not go sufficiently beyond previous work.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Selfless Sequential Learning
Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations, our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets.
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>



 [REVISION] The work is thorough and some of my minor concerns have been addressed, so I am increasing my score to 6. I cannot go beyond because of the incremental nature of the work, and the very limited applicability of the used continual learning setup from this paper. [OLD REVIEW] The paper proposes a novel, regularization based, approach to the sequential learning problem using a fixed size model. The main idea is to add extra terms to the loss encouraging representation sparsity and combating catastrophic forgetting. The approach fairs well compared to other regularization based approaches on MNIST and CIFAR-100 sequential learning variants. Pros: Thorough experiments, competitive baselines and informative ablation study. Good performance on par or superior to baselines. Clear paper, well written. Cons: The approach, while competitive in performance, does not seem to fix any significant issues with baseline methods. For example, task boundaries are still used, which limits applicability; in many scenarios which do have a continual learning problem there are no clear task boundaries, such as data distribution drift in both supervised and reinforcement learning. Since models used in the work are very different from SOTA models on those particular tasks, it is hard to determine from the paper how the proposed method influences these models. In particular, it is not clear whether these changes to the loss would still allow top performance on regular classification tasks, e.g. CIFAR-10 or MNIST even without sequential learning, or in multitask learning settings. Summary: Although the work is substantial and experiments are thorough, I have reservations about extrapolating from the results to settings which do have a continual learning problem. Although I am convinced results are slightly superior to baselines, and I appreciate the lengthy amount of work which went into proving that, the paper does not go sufficiently beyond previous work.
assistant
 [REVISION] The work is thorough and some of my minor concerns have been addressed, so I am increasing my score to 6. I cannot go beyond because of the incremental nature of the work, and the very limited applicability of the used continual learning setup from this paper. [OLD REVIEW] The paper proposes a novel, regularization based, approach to the sequential learning problem using a fixed size model. The main idea is to add extra terms to the loss encouraging representation sparsity and combating catastrophic forgetting. The approach fairs well compared to other regularization based approaches on MNIST and CIFAR-100 sequential learning variants. Pros: Thorough experiments, competitive baselines and informative ablation study. Good performance on par or superior to baselines. Clear paper, well written. Cons: The approach, while competitive in performance, does not seem to fix any significant issues with baseline methods. For example, task boundaries are still used, which limits applicability; in many scenarios which do have a continual learning problem there are no clear task boundaries, such as data distribution drift in both supervised and reinforcement learning. Since models used in the work are very different from SOTA models on those particular tasks, it is hard to determine from the paper how the proposed method influences these models. In particular, it is not clear whether these changes to the loss would still allow top performance on regular classification tasks, e.g. CIFAR-10 or MNIST even without sequential learning, or in multitask learning settings. Summary: Although the work is substantial and experiments are thorough, I have reservations about extrapolating from the results to settings which do have a continual learning problem. Although I am convinced results are slightly superior to baselines, and I appreciate the lengthy amount of work which went into proving that, the paper does not go sufficiently beyond previous work.