A comprehensive, application-oriented study of catastrophic forgetting in DNNs
We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experimental protocol is proposed that takes into account typical constraints encountered in application scenarios. As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF. Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions. We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.
 The paper presents a study of the application of some well known methods on 9 datasets focusing on the issue of catastrophic forgetting when considering a sequential learning task in them. In general the presentation of concepts and results is a bit problematic and unclear. Comments, such that the paper presents * a novel training and model selection paradigm for incremental learning in DNNs * is not justified. A better description of the results, e.g., in Table 3 should be presented, as well a better linking with the findings; a better structure of the latter would also be required to improve consistency of them. Improving these could make the paper candidate for a poster presentation.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
A comprehensive, application-oriented study of catastrophic forgetting in DNNs
We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning. A new experimental protocol is proposed that takes into account typical constraints encountered in application scenarios. As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF. Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions. We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.
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 presents a study of the application of some well known methods on 9 datasets focusing on the issue of catastrophic forgetting when considering a sequential learning task in them. In general the presentation of concepts and results is a bit problematic and unclear. Comments, such that the paper presents * a novel training and model selection paradigm for incremental learning in DNNs * is not justified. A better description of the results, e.g., in Table 3 should be presented, as well a better linking with the findings; a better structure of the latter would also be required to improve consistency of them. Improving these could make the paper candidate for a poster presentation.
assistant
 The paper presents a study of the application of some well known methods on 9 datasets focusing on the issue of catastrophic forgetting when considering a sequential learning task in them. In general the presentation of concepts and results is a bit problematic and unclear. Comments, such that the paper presents * a novel training and model selection paradigm for incremental learning in DNNs * is not justified. A better description of the results, e.g., in Table 3 should be presented, as well a better linking with the findings; a better structure of the latter would also be required to improve consistency of them. Improving these could make the paper candidate for a poster presentation.