An Analysis of Feature Regularization for Low-shot Learning
Low-shot visual learning, the ability to recognize novel object categories from very few, or even one example, is a hallmark of human visual intelligence. Though successful on many tasks, deep learning approaches tends to be notoriously data-hungry. Recently, feature penalty regularization has been proved effective on capturing new concepts. In this work, we provide both empirical evidence and theoretical analysis on how and why these methods work. We also propose a better design of cost function with improved performance. Close scrutiny reveals the centering effect of feature representation, as well as the intrinsic connection with batch normalization. Extensive experiments on synthetic datasets, the one-shot learning benchmark “Omniglot”, and large-scale ImageNet validate our analysis.
This paper proposes analysis of regularization, weight Froebius-norm and feature L2 norm, showing that it is equivalent to another proposed regularization, gradient magnitude loss. They then argue that: 1) it is helpful to low-shot learning, 2) it is numerically stable, 3) it is a soft version of Batch Normalization. Finally, they demonstrate experimentally that such a regularization improves performance on low-shot tasks. First, this is a nice analysis of some simple models, and proposes interesting insights in some optimization issues. Unfortunately, the authors do not demonstrate, nor argue in a convincing manner, that such an analysis extends to deep non-linear computation structures. I feel like the authors could write a full paper about *results can be derived for ?(x) with convex differentiable non-linear activation functions such as ReLU*, both via analysis and experimentation to measure numerical stability. Second, the authors again show an interesting correspondance to batch normalization, but IMO fail to experimentally show its relevance. Finally, I understand the appeal of the proposed method from a numerical stability point of view, but am not convinced that it has any effect on low-shot learning in the high dimensional spaces that deep networks are used for. I commend the authors for contributing to the mathematical understanding of our field, but I think they have yet to demonstrate the large scale effectiveness of what they propose. At the same time, I feel like this paper does not have a clear and strong message. It makes various (interesting) claims about a number of things, but they seem more or less disparate, and only loosely related to low-shot learning. notes: - *an expectation taken with respect to the empirical distribution generated by the training set*, generally the training set is viewed as a *montecarlo* sample of the underlying, unknown data distribution mathcal{D}. - *we can see that our model learns meaningful representations*, it gets a 6.5% improvement on the baseline, but there is no analysis of the meaningfulness of the representations. - *Table 13.2* should be *Table 2*. - please be mindful of formatting, some citations should be parenthesized and there are numerous extraneous and missing spacings between words and sentences.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
An Analysis of Feature Regularization for Low-shot Learning
Low-shot visual learning, the ability to recognize novel object categories from very few, or even one example, is a hallmark of human visual intelligence. Though successful on many tasks, deep learning approaches tends to be notoriously data-hungry. Recently, feature penalty regularization has been proved effective on capturing new concepts. In this work, we provide both empirical evidence and theoretical analysis on how and why these methods work. We also propose a better design of cost function with improved performance. Close scrutiny reveals the centering effect of feature representation, as well as the intrinsic connection with batch normalization. Extensive experiments on synthetic datasets, the one-shot learning benchmark “Omniglot”, and large-scale ImageNet validate our analysis.
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 analysis of regularization, weight Froebius-norm and feature L2 norm, showing that it is equivalent to another proposed regularization, gradient magnitude loss. They then argue that: 1) it is helpful to low-shot learning, 2) it is numerically stable, 3) it is a soft version of Batch Normalization. Finally, they demonstrate experimentally that such a regularization improves performance on low-shot tasks. First, this is a nice analysis of some simple models, and proposes interesting insights in some optimization issues. Unfortunately, the authors do not demonstrate, nor argue in a convincing manner, that such an analysis extends to deep non-linear computation structures. I feel like the authors could write a full paper about *results can be derived for ?(x) with convex differentiable non-linear activation functions such as ReLU*, both via analysis and experimentation to measure numerical stability. Second, the authors again show an interesting correspondance to batch normalization, but IMO fail to experimentally show its relevance. Finally, I understand the appeal of the proposed method from a numerical stability point of view, but am not convinced that it has any effect on low-shot learning in the high dimensional spaces that deep networks are used for. I commend the authors for contributing to the mathematical understanding of our field, but I think they have yet to demonstrate the large scale effectiveness of what they propose. At the same time, I feel like this paper does not have a clear and strong message. It makes various (interesting) claims about a number of things, but they seem more or less disparate, and only loosely related to low-shot learning. notes: - *an expectation taken with respect to the empirical distribution generated by the training set*, generally the training set is viewed as a *montecarlo* sample of the underlying, unknown data distribution mathcal{D}. - *we can see that our model learns meaningful representations*, it gets a 6.5% improvement on the baseline, but there is no analysis of the meaningfulness of the representations. - *Table 13.2* should be *Table 2*. - please be mindful of formatting, some citations should be parenthesized and there are numerous extraneous and missing spacings between words and sentences.
assistant
This paper proposes analysis of regularization, weight Froebius-norm and feature L2 norm, showing that it is equivalent to another proposed regularization, gradient magnitude loss. They then argue that: 1) it is helpful to low-shot learning, 2) it is numerically stable, 3) it is a soft version of Batch Normalization. Finally, they demonstrate experimentally that such a regularization improves performance on low-shot tasks. First, this is a nice analysis of some simple models, and proposes interesting insights in some optimization issues. Unfortunately, the authors do not demonstrate, nor argue in a convincing manner, that such an analysis extends to deep non-linear computation structures. I feel like the authors could write a full paper about *results can be derived for ? (x) with convex differentiable non-linear activation functions such as ReLU*, both via analysis and experimentation to measure numerical stability. Second, the authors again show an interesting correspondance to batch normalization, but IMO fail to experimentally show its relevance. Finally, I understand the appeal of the proposed method from a numerical stability point of view, but am not convinced that it has any effect on low-shot learning in the high dimensional spaces that deep networks are used for. I commend the authors for contributing to the mathematical understanding of our field, but I think they have yet to demonstrate the large scale effectiveness of what they propose. At the same time, I feel like this paper does not have a clear and strong message. It makes various (interesting) claims about a number of things, but they seem more or less disparate, and only loosely related to low-shot learning. notes: - *an expectation taken with respect to the empirical distribution generated by the training set*, generally the training set is viewed as a *montecarlo* sample of the underlying, unknown data distribution mathcal{D}. - *we can see that our model learns meaningful representations*, it gets a 6.5% improvement on the baseline, but there is no analysis of the meaningfulness of the representations. - *Table 13.2* should be *Table 2*. - please be mindful of formatting, some citations should be parenthesized and there are numerous extraneous and missing spacings between words and sentences.