An Empirical Study of Example Forgetting during Deep Neural Network Learning
Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a ``forgetting event** to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set*s (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.
 This is an excellent analysis paper of a very interesting phenomenon in deep neural networks. Quality, Clarity, Originality: As far as I know, the paper explores a very relevant and original question -- studying how the learning process of different examples in the dataset varies. In particular, the authors study whether some examples are harder to learn than others (examples that are forgotten and relearned multiple times through learning.) We can imagine that such examples are "support vectors" for neural networks, helping define the decision boundary. The paper is very clear and the experiments are of very high quality. I particularly appreciated the effort of the authors to use architectures that achieve close to SOTA on all datasets to ensure conclusions are valid in this setting. I also thought the multiple repetitions and analysing rank correlation over different random seeds was a good additional test. Significance This paper has some very interesting and significant takeaways. Some of the other experiments I thought were particularly insightful were the effect on test error of removing examples that aren*t forgotten to examples that are forgotten more. In summary, the "harder" examples are more crucial to define the right decision boundaries. I also liked the experiment with noisy labels, showing that this results in networks forgetting faster. My one suggestion would be to try this experiment with noisy *data* instead of noisy labels, as we are especially curious about the effect of the data (as opposed to a different labelling task.) I encourage the authors to followup with a larger scaled version of their experiments. It*s possible that for a harder task like Imagenet, a combination of "easy" and "hard" examples might be needed to enable learning and define good decision boundaries. I argue strongly for this paper to be accepted to ICLR, I think it will be of great interest to the community.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a ``forgetting event** to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set*s (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.
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 is an excellent analysis paper of a very interesting phenomenon in deep neural networks. Quality, Clarity, Originality: As far as I know, the paper explores a very relevant and original question -- studying how the learning process of different examples in the dataset varies. In particular, the authors study whether some examples are harder to learn than others (examples that are forgotten and relearned multiple times through learning.) We can imagine that such examples are "support vectors" for neural networks, helping define the decision boundary. The paper is very clear and the experiments are of very high quality. I particularly appreciated the effort of the authors to use architectures that achieve close to SOTA on all datasets to ensure conclusions are valid in this setting. I also thought the multiple repetitions and analysing rank correlation over different random seeds was a good additional test. Significance This paper has some very interesting and significant takeaways. Some of the other experiments I thought were particularly insightful were the effect on test error of removing examples that aren*t forgotten to examples that are forgotten more. In summary, the "harder" examples are more crucial to define the right decision boundaries. I also liked the experiment with noisy labels, showing that this results in networks forgetting faster. My one suggestion would be to try this experiment with noisy *data* instead of noisy labels, as we are especially curious about the effect of the data (as opposed to a different labelling task.) I encourage the authors to followup with a larger scaled version of their experiments. It*s possible that for a harder task like Imagenet, a combination of "easy" and "hard" examples might be needed to enable learning and define good decision boundaries. I argue strongly for this paper to be accepted to ICLR, I think it will be of great interest to the community.
assistant
 This is an excellent analysis paper of a very interesting phenomenon in deep neural networks. Quality, Clarity, Originality: As far as I know, the paper explores a very relevant and original question -- studying how the learning process of different examples in the dataset varies. In particular, the authors study whether some examples are harder to learn than others (examples that are forgotten and relearned multiple times through learning.) We can imagine that such examples are "support vectors" for neural networks, helping define the decision boundary. The paper is very clear and the experiments are of very high quality. I particularly appreciated the effort of the authors to use architectures that achieve close to SOTA on all datasets to ensure conclusions are valid in this setting. I also thought the multiple repetitions and analysing rank correlation over different random seeds was a good additional test. Significance This paper has some very interesting and significant takeaways. Some of the other experiments I thought were particularly insightful were the effect on test error of removing examples that aren*t forgotten to examples that are forgotten more. In summary, the "harder" examples are more crucial to define the right decision boundaries. I also liked the experiment with noisy labels, showing that this results in networks forgetting faster. My one suggestion would be to try this experiment with noisy *data* instead of noisy labels, as we are especially curious about the effect of the data (as opposed to a different labelling task.) I encourage the authors to followup with a larger scaled version of their experiments. It*s possible that for a harder task like Imagenet, a combination of "easy" and "hard" examples might be needed to enable learning and define good decision boundaries. I argue strongly for this paper to be accepted to ICLR, I think it will be of great interest to the community.