Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Neural network pruning techniques have demonstrated it is possible to remove the majority of weights in a network with surprisingly little degradation to top-1 test set accuracy. However, this measure of performance conceals significant differences in how different classes and images are impacted by pruning. We find that certain individual data points, which we term pruning identified exemplars (PIEs), and classes are systematically more impacted by the introduction of sparsity. Removing PIE images from the test-set greatly improves top-1 accuracy for both sparse and non-sparse models. These hard-to-generalize-to images tend to be of lower image quality, mislabelled, entail abstract representations, require fine-grained classification or depict atypical class examples.
This paper presents an empirical study on the effect of pruning to the model performance on each class and example, which leads to a novel finding that it has disparate effects to each sample. Specifically, the authors have found out that examples that are affected the most by pruning are more difficult to classify even for the non-pruned network, due to low image quality, mislabeling, or being atypical from the class prototype, and performed a further human study to analyze the source of difficulty. Moreover, the authors performed an additional experiment, which shows that the sparse models are brittle against natural adversarial examples. Pros - The paper provides a novel insight on the effect of pruning at the class and the example level, which could lead to a more effective pruning approach that exploit this findings. Cons - The paper only provides a novel finding but not the solution on how to tackle this problem, and thus the paper looks incomplete. After section 3.3, I was expecting to see some approaches to tackle this problem but the paper abruptly ended. - The effect of pruning could largely differ from one method to another, but the authors do not experimentally compare the effects of different pruning methods. Also, it is highly likely that the findings discussed in the paper may be only true for input-independent pruning approaches, and may not generalize to input-dependent pruning method. The authors need to perform extensive study of both input-dependent and input-independent pruning approaches to validate their points. In sum, the paper provides a novel insight on how pruning affects the performance at the example level, but does not provide a solution, and the current set of experiments is insufficient to validate that the empirical findings that the authors report generalize to other types of pruning approaches, such as input-dependent pruning. Thus I believe that the paper is not ready for publication yet, and vote for rejecting this paper in its current form.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Neural network pruning techniques have demonstrated it is possible to remove the majority of weights in a network with surprisingly little degradation to top-1 test set accuracy. However, this measure of performance conceals significant differences in how different classes and images are impacted by pruning. We find that certain individual data points, which we term pruning identified exemplars (PIEs), and classes are systematically more impacted by the introduction of sparsity. Removing PIE images from the test-set greatly improves top-1 accuracy for both sparse and non-sparse models. These hard-to-generalize-to images tend to be of lower image quality, mislabelled, entail abstract representations, require fine-grained classification or depict atypical class examples.
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>



This paper presents an empirical study on the effect of pruning to the model performance on each class and example, which leads to a novel finding that it has disparate effects to each sample. Specifically, the authors have found out that examples that are affected the most by pruning are more difficult to classify even for the non-pruned network, due to low image quality, mislabeling, or being atypical from the class prototype, and performed a further human study to analyze the source of difficulty. Moreover, the authors performed an additional experiment, which shows that the sparse models are brittle against natural adversarial examples. Pros - The paper provides a novel insight on the effect of pruning at the class and the example level, which could lead to a more effective pruning approach that exploit this findings. Cons - The paper only provides a novel finding but not the solution on how to tackle this problem, and thus the paper looks incomplete. After section 3.3, I was expecting to see some approaches to tackle this problem but the paper abruptly ended. - The effect of pruning could largely differ from one method to another, but the authors do not experimentally compare the effects of different pruning methods. Also, it is highly likely that the findings discussed in the paper may be only true for input-independent pruning approaches, and may not generalize to input-dependent pruning method. The authors need to perform extensive study of both input-dependent and input-independent pruning approaches to validate their points. In sum, the paper provides a novel insight on how pruning affects the performance at the example level, but does not provide a solution, and the current set of experiments is insufficient to validate that the empirical findings that the authors report generalize to other types of pruning approaches, such as input-dependent pruning. Thus I believe that the paper is not ready for publication yet, and vote for rejecting this paper in its current form.
assistant
This paper presents an empirical study on the effect of pruning to the model performance on each class and example, which leads to a novel finding that it has disparate effects to each sample. Specifically, the authors have found out that examples that are affected the most by pruning are more difficult to classify even for the non-pruned network, due to low image quality, mislabeling, or being atypical from the class prototype, and performed a further human study to analyze the source of difficulty. Moreover, the authors performed an additional experiment, which shows that the sparse models are brittle against natural adversarial examples. Pros - The paper provides a novel insight on the effect of pruning at the class and the example level, which could lead to a more effective pruning approach that exploit this findings. Cons - The paper only provides a novel finding but not the solution on how to tackle this problem, and thus the paper looks incomplete. After section 3.3, I was expecting to see some approaches to tackle this problem but the paper abruptly ended. - The effect of pruning could largely differ from one method to another, but the authors do not experimentally compare the effects of different pruning methods. Also, it is highly likely that the findings discussed in the paper may be only true for input-independent pruning approaches, and may not generalize to input-dependent pruning method. The authors need to perform extensive study of both input-dependent and input-independent pruning approaches to validate their points. In sum, the paper provides a novel insight on how pruning affects the performance at the example level, but does not provide a solution, and the current set of experiments is insufficient to validate that the empirical findings that the authors report generalize to other types of pruning approaches, such as input-dependent pruning. Thus I believe that the paper is not ready for publication yet, and vote for rejecting this paper in its current form.