Paper:
ON THE IMPORTANCE OF SINGLE DIRECTIONS FOR GENERALIZATION
Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance. However, the differences between the learned solutions of networks which generalize and those which do not remain unclear. Additionally, the tuning properties of single directions (defined as the activation of a single unit or some linear combination of units in response to some input) have been highlighted, but their importance has not been evaluated. Here, we connect these lines of inquiry to demonstrate that a network's reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyperparameters, and over the course of training. While dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units. Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.

Review:
article summary: 
The authors use ablation analyses to evaluate the reliance on single coordinate-aligned directions in activation space (i.e. the activation of single units or feature maps) as a function of memorization. They find that the performance of networks that memorize more are also more affected by ablations. This result holds even for identical networks trained on identical data. The dynamics of this reliance on single directions suggest that it could be used as a criterion for early stopping. The authors discuss this observation in relation to dropout and batch normalization. Although dropout is an effective regularizer to prevent memorization of random labels, it does not prevent over-reliance on single directions. Batch normalization does appear to reduce the reliance on single directions, providing an alternative explanation for the effectiveness of batch normalization. Networks trained without batch normalization also demonstrated a significantly higher amount of class selectivity in individual units compared to networks trained without batch normalization. Highly selective units were found to be no more important than units that were not selective to a particular class. These results suggest that highly selective units may actually be harmful to network performance. 
* Quality: The paper presents thorough and careful empirical analyses to support their claims.
* Clarity: The paper is very clear and well-organized. Sufficient detail is provided to reproduce the results.
* Originality: This work is one of many recent papers trying to understand generalization in deep networks. Their description of the activation space of networks that generalize compared to those that memorize is novel. The authors throughly relate their findings to related work on generalization, regularization, and pruning. However, the authors may wish to relate their findings to recent reports in neuroscience observing similar phenomena (see below).
* Significance: The paper provides valuable insight that helps to relate existing theories about generalization in deep networks. The insights of this paper will have a large impact on regularization, early stopping, generalization, and methods used to explain neural networks. 
Pros:
* Observations are replicated for several network architectures and datasets. 
* Observations are very clearly contextualized with respect to several active areas of deep learning research.
Cons:
* The class selectivity measure does not capture all class-related information that a unit may pass on. 
Comments:
* Regarding the class selectivity of single units, there is a growing body of literature in neurophysiology and neuroimaging describing similar observations where the interpretation has been that a primary role of any neural pathway is to “denoise” or cancel out the “distractor” rather than just amplifying the “signal” of interest. 
    * Untuned But Not Irrelevant: The Role of Untuned Neurons In Sensory Information Coding, https://www.biorxiv.org/content/early/2017/09/21/134379
    * Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles https://www.ncbi.nlm.nih.gov/pubmed/28275096
    * On the interpretation of weight vectors of linear models in multivariate neuroimaging http://www.sciencedirect.com/science/article/pii/S1053811913010914
        * see also LEARNING HOW TO EXPLAIN NEURAL NETWORKS https://openreview.net/forum?id=Hkn7CBaTW
* Regarding the intuition in section 3.1, "The minimal description length of the model should be larger for the memorizing network than for the structure- finding network. As a result, the memorizing network should use more of its capacity than the structure-finding network, and by extension, more single directions”. Does reliance on single directions not also imply a local encoding scheme? We know that for a fixed number of units, a distributed representation will be able to encode a larger number of unique items than a local one. Therefore if this behaviour was the result of needing to use up more of the capacity of the network, wouldn’t you expect to observe more distributed representations? 
Minor issues:
* In the first sentence of section 2.3, you say you analyzed three models and then you only list two. It seems you forgot to include ResNet trained on ImageNet.