Understanding intermediate layers using linear classifier probes
Neural network models have a reputation for being black boxes. We propose a new method to better understand the roles and dynamics of the intermediate layers. This has direct consequences on the design of such models and it enables the expert to be able to justify certain heuristics (such as adding auxiliary losses in middle layers). Our method uses linear classifiers, referred to as ``probes**, where a probe can only use the hidden units of a given intermediate layer as discriminating features. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. They allow the user to visualize the state of the model at multiple steps of training. We demonstrate how this can be used to develop a better intuition about models and to diagnose potential problems.
The authors propose a method to investigate the predictiveness of intermediate layer activations. To do so, they propose training linear classifiers and evaluate the error on the test set. The paper is well motivated and aims to shed some light onto the progress of model training and hopes to provide insights into deep learning architecture design. The two main reasons for why the authors decided to use linear probes seem to be: - convexity - The last layer in the network is (usually) linear In the second to last paragraph of page 4 the authors point out that it could happen that the intermediate features are useless for a linear classifier. This is correct and what I consider the main flaw of the paper. I am missing any motivation as to the usefulness of the suggested analysis to architecture design. In fact, the example with the skip connection (Figure 8) seems to suggest that skip connections shouldn*t be used. Doesn*t that contradict the recent successes of ResNet? While the results are interesting, they aren*t particularly surprising and I am failing to see direct applicability to understanding deep models as the authors suggest.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Understanding intermediate layers using linear classifier probes
Neural network models have a reputation for being black boxes. We propose a new method to better understand the roles and dynamics of the intermediate layers. This has direct consequences on the design of such models and it enables the expert to be able to justify certain heuristics (such as adding auxiliary losses in middle layers). Our method uses linear classifiers, referred to as ``probes**, where a probe can only use the hidden units of a given intermediate layer as discriminating features. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. They allow the user to visualize the state of the model at multiple steps of training. We demonstrate how this can be used to develop a better intuition about models and to diagnose potential problems.
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 authors propose a method to investigate the predictiveness of intermediate layer activations. To do so, they propose training linear classifiers and evaluate the error on the test set. The paper is well motivated and aims to shed some light onto the progress of model training and hopes to provide insights into deep learning architecture design. The two main reasons for why the authors decided to use linear probes seem to be: - convexity - The last layer in the network is (usually) linear In the second to last paragraph of page 4 the authors point out that it could happen that the intermediate features are useless for a linear classifier. This is correct and what I consider the main flaw of the paper. I am missing any motivation as to the usefulness of the suggested analysis to architecture design. In fact, the example with the skip connection (Figure 8) seems to suggest that skip connections shouldn*t be used. Doesn*t that contradict the recent successes of ResNet? While the results are interesting, they aren*t particularly surprising and I am failing to see direct applicability to understanding deep models as the authors suggest.
assistant
The authors propose a method to investigate the predictiveness of intermediate layer activations. To do so, they propose training linear classifiers and evaluate the error on the test set. The paper is well motivated and aims to shed some light onto the progress of model training and hopes to provide insights into deep learning architecture design. The two main reasons for why the authors decided to use linear probes seem to be: - convexity - The last layer in the network is (usually) linear In the second to last paragraph of page 4 the authors point out that it could happen that the intermediate features are useless for a linear classifier. This is correct and what I consider the main flaw of the paper. I am missing any motivation as to the usefulness of the suggested analysis to architecture design. In fact, the example with the skip connection (Figure 8) seems to suggest that skip connections shouldn*t be used. Doesn*t that contradict the recent successes of ResNet? While the results are interesting, they aren*t particularly surprising and I am failing to see direct applicability to understanding deep models as the authors suggest.