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.
This paper proposes to use a linear classifier as the probe for the informativeness of the hidden activations from different neural network layers. The training of the linear classifier does not affect the training of the neural network. The paper is well motivated for investigating how much useful information (or how good the representations are) for each layer. The observations in this paper agrees with existing insights, such as, 1) (Fig 5a) too many random layers are harmful. 2) (Fig 5b) training is helpful. 3) (Fig 7) lower layers converge faster than higher layer. 4) (Fig 8) too deep network is hard to train, and skip link can remedy this problem. However, this paper has following problems: 1. It is not sufficiently justified why the linear classifier is a good probe. It is not crystal clear why good intermediate features need to show high linear classification accuracy. More theoretical analysis and/or intuition will be helpful. 2. This paper does not provide much insight on how to design better networks based on the observations. Designing a better network is also the best way to justify the usefulness of the analysis. Overall, this paper is tackling an interesting problem, but the technique (the linear classifier as the probe) is not novel and more importantly need to be better justified. Moreover, it is important to show how to design better neural networks using the observations in this paper.
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>



This paper proposes to use a linear classifier as the probe for the informativeness of the hidden activations from different neural network layers. The training of the linear classifier does not affect the training of the neural network. The paper is well motivated for investigating how much useful information (or how good the representations are) for each layer. The observations in this paper agrees with existing insights, such as, 1) (Fig 5a) too many random layers are harmful. 2) (Fig 5b) training is helpful. 3) (Fig 7) lower layers converge faster than higher layer. 4) (Fig 8) too deep network is hard to train, and skip link can remedy this problem. However, this paper has following problems: 1. It is not sufficiently justified why the linear classifier is a good probe. It is not crystal clear why good intermediate features need to show high linear classification accuracy. More theoretical analysis and/or intuition will be helpful. 2. This paper does not provide much insight on how to design better networks based on the observations. Designing a better network is also the best way to justify the usefulness of the analysis. Overall, this paper is tackling an interesting problem, but the technique (the linear classifier as the probe) is not novel and more importantly need to be better justified. Moreover, it is important to show how to design better neural networks using the observations in this paper.
assistant
This paper proposes to use a linear classifier as the probe for the informativeness of the hidden activations from different neural network layers. The training of the linear classifier does not affect the training of the neural network. The paper is well motivated for investigating how much useful information (or how good the representations are) for each layer. The observations in this paper agrees with existing insights, such as, 1) (Fig 5a) too many random layers are harmful. 2) (Fig 5b) training is helpful. 3) (Fig 7) lower layers converge faster than higher layer. 4) (Fig 8) too deep network is hard to train, and skip link can remedy this problem. However, this paper has following problems: 1. It is not sufficiently justified why the linear classifier is a good probe. It is not crystal clear why good intermediate features need to show high linear classification accuracy. More theoretical analysis and/or intuition will be helpful. 2. This paper does not provide much insight on how to design better networks based on the observations. Designing a better network is also the best way to justify the usefulness of the analysis. Overall, this paper is tackling an interesting problem, but the technique (the linear classifier as the probe) is not novel and more importantly need to be better justified. Moreover, it is important to show how to design better neural networks using the observations in this paper.