Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
Deep learning for object classification relies heavily on convolutional models. While effective, CNNs are rarely interpretable after the fact. An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification. We expand on this idea by forcing the method to explicitly align images to be classified to reference images representing the classes. The mechanism of alignment is learned and therefore does not require that the reference objects are anything like those being classified. Beyond explanation, our exemplar based cross-alignment method enables classification with only a single example per category (one-shot). Our model cuts the 5-way, 1-shot error rate in Omniglot from 2.1\% to 1.4\% and in MiniImageNet from 53.5\% to 46.5\% while simultaneously providing point-wise alignment information providing some understanding on what the network is capturing. This method of alignment also enables the recognition of an unsupported class (open-set) in the one-shot setting while maintaining an F1-score of above 0.5 for Omniglot even with 19 other distracting classes while baselines completely fail to separate the open-set class in the one-shot setting.
 The authors propose a deep learning method based on image alignment to perform one-shot classification and open-set recognition. The proposed model is an extension of Matching Networks [Vinyals et al., 2016] where a different image embedding is adopted and a pixel-wise alignment step between test and reference image is added to the architecture. The work relies on two strong assumptions: (i) to consider each point mapping as independent, and (ii) to consider the correct alignment much more likely than the incorrect ones. The manuscript doesn’t report arguments in favour of these assumptions. The motivation is partially covered by your statement “marginalizing over all possible matching is intractable”, nevertheless an explanation of why it is reasonable to introduce these assumptions is not clearly stated. The self-regularization allows the model to have a performance improvement, and it is considered one of the contribution of this work. Nevertheless the manuscript doesn’t provide a detailed explanation on how the self regularization is designed. For example it is not clear whether the 10% and 20% pixel sampling is applied also during self regularization. The model is computationally very expensive and force the use of only 10% of the target image pixels and 20% of the reference images’ pixels. The complexity is intrinsic of the pixel-wise alignment formulation, but in any case this approximation is a relevant approximation that is never justified. The use of hyper column descriptors is an effective workaround to achieve good performance even though this approximation. The discussion is neglecting to argue this aspect. One motivation for proposing an alignment-based matching is a better explanation of results. The tacit assumption of the authors is that a classifier driven by a point-wise alignment may improve the interpretation. The random uniformly distributed subsampling of pixels makes the model less interpretable.It may occur for example as shown in figure 3 where the model finds some points that for human interpretation are not relevant and at the same time these points are matched with points that have some semantic meaning.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Alignment Based Mathching Networks for One-Shot Classification and Open-Set Recognition
Deep learning for object classification relies heavily on convolutional models. While effective, CNNs are rarely interpretable after the fact. An attention mechanism can be used to highlight the area of the image that the model focuses on thus offering a narrow view into the mechanism of classification. We expand on this idea by forcing the method to explicitly align images to be classified to reference images representing the classes. The mechanism of alignment is learned and therefore does not require that the reference objects are anything like those being classified. Beyond explanation, our exemplar based cross-alignment method enables classification with only a single example per category (one-shot). Our model cuts the 5-way, 1-shot error rate in Omniglot from 2.1\% to 1.4\% and in MiniImageNet from 53.5\% to 46.5\% while simultaneously providing point-wise alignment information providing some understanding on what the network is capturing. This method of alignment also enables the recognition of an unsupported class (open-set) in the one-shot setting while maintaining an F1-score of above 0.5 for Omniglot even with 19 other distracting classes while baselines completely fail to separate the open-set class in the one-shot setting.
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 deep learning method based on image alignment to perform one-shot classification and open-set recognition. The proposed model is an extension of Matching Networks [Vinyals et al., 2016] where a different image embedding is adopted and a pixel-wise alignment step between test and reference image is added to the architecture. The work relies on two strong assumptions: (i) to consider each point mapping as independent, and (ii) to consider the correct alignment much more likely than the incorrect ones. The manuscript doesn’t report arguments in favour of these assumptions. The motivation is partially covered by your statement “marginalizing over all possible matching is intractable”, nevertheless an explanation of why it is reasonable to introduce these assumptions is not clearly stated. The self-regularization allows the model to have a performance improvement, and it is considered one of the contribution of this work. Nevertheless the manuscript doesn’t provide a detailed explanation on how the self regularization is designed. For example it is not clear whether the 10% and 20% pixel sampling is applied also during self regularization. The model is computationally very expensive and force the use of only 10% of the target image pixels and 20% of the reference images’ pixels. The complexity is intrinsic of the pixel-wise alignment formulation, but in any case this approximation is a relevant approximation that is never justified. The use of hyper column descriptors is an effective workaround to achieve good performance even though this approximation. The discussion is neglecting to argue this aspect. One motivation for proposing an alignment-based matching is a better explanation of results. The tacit assumption of the authors is that a classifier driven by a point-wise alignment may improve the interpretation. The random uniformly distributed subsampling of pixels makes the model less interpretable.It may occur for example as shown in figure 3 where the model finds some points that for human interpretation are not relevant and at the same time these points are matched with points that have some semantic meaning.
assistant
 The authors propose a deep learning method based on image alignment to perform one-shot classification and open-set recognition. The proposed model is an extension of Matching Networks [Vinyals et al., 2016] where a different image embedding is adopted and a pixel-wise alignment step between test and reference image is added to the architecture. The work relies on two strong assumptions: (i) to consider each point mapping as independent, and (ii) to consider the correct alignment much more likely than the incorrect ones. The manuscript doesn’t report arguments in favour of these assumptions. The motivation is partially covered by your statement “marginalizing over all possible matching is intractable”, nevertheless an explanation of why it is reasonable to introduce these assumptions is not clearly stated. The self-regularization allows the model to have a performance improvement, and it is considered one of the contribution of this work. Nevertheless the manuscript doesn’t provide a detailed explanation on how the self regularization is designed. For example it is not clear whether the 10% and 20% pixel sampling is applied also during self regularization. The model is computationally very expensive and force the use of only 10% of the target image pixels and 20% of the reference images’ pixels. The complexity is intrinsic of the pixel-wise alignment formulation, but in any case this approximation is a relevant approximation that is never justified. The use of hyper column descriptors is an effective workaround to achieve good performance even though this approximation. The discussion is neglecting to argue this aspect. One motivation for proposing an alignment-based matching is a better explanation of results. The tacit assumption of the authors is that a classifier driven by a point-wise alignment may improve the interpretation. The random uniformly distributed subsampling of pixels makes the model less interpretable.It may occur for example as shown in figure 3 where the model finds some points that for human interpretation are not relevant and at the same time these points are matched with points that have some semantic meaning.