Deep Network classification by Scattering and Homotopy dictionary learning
We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet ILSVRC2012 dataset. The network first applies a scattering transform which linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse l1 dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework including ALISTA. Classification results are analyzed over ImageNet.
The paper proposes a network architecture composed of three interpretable components followed by a simple MLP classifier. It first applies a scattering transform followed by a learned linear projection (to reduce dimensionality). A sparse representation of these coefficients is then obtained using dictionary learning. The projection, dictionary and MLP classifier are jointly trained to minimize the classification loss. Results show that the model outperforms AlexNet on the Imagenet benchmark. The paper is well written. The main contribution of the work is to present an architecture with composed of mathematically interpretable components achieving very high empirical performance. I find that these results are very significant. The second contribution is to propose a dictionary learning algorithm that uses ISTC and can be trained with gradient descent. I think that it would be interesting to add an ablation showing the change in performance by changing N (the number of iterations in the ISTC net). Also, it would make sense to run FISTA or ISTA unrolls to see if the benefits of the faster convergence also affect classification performance. It would be good to add to Table 1 the number of trainable parameters of each variant. I find it a bit confusing to refer to the setting in which W is learned as ALISTA, as to me ALISTA implies using analytical W. This is clear later in the text (and makes sense from a computational standpoint). Would be good to clarify it early in the text. Finally the paper presents a proof of exponential convergence for ALISTA in the noisy case. While this is an interesting result, it is not very closely linked to the main focus of the work.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Network classification by Scattering and Homotopy dictionary learning
We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet ILSVRC2012 dataset. The network first applies a scattering transform which linearizes variabilities due to geometric transformations such as translations and small deformations. A sparse l1 dictionary coding reduces intra-class variability while preserving class separation through projections over unions of linear spaces. It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence. A convergence proof is given in a general framework including ALISTA. Classification results are analyzed over ImageNet.
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 paper proposes a network architecture composed of three interpretable components followed by a simple MLP classifier. It first applies a scattering transform followed by a learned linear projection (to reduce dimensionality). A sparse representation of these coefficients is then obtained using dictionary learning. The projection, dictionary and MLP classifier are jointly trained to minimize the classification loss. Results show that the model outperforms AlexNet on the Imagenet benchmark. The paper is well written. The main contribution of the work is to present an architecture with composed of mathematically interpretable components achieving very high empirical performance. I find that these results are very significant. The second contribution is to propose a dictionary learning algorithm that uses ISTC and can be trained with gradient descent. I think that it would be interesting to add an ablation showing the change in performance by changing N (the number of iterations in the ISTC net). Also, it would make sense to run FISTA or ISTA unrolls to see if the benefits of the faster convergence also affect classification performance. It would be good to add to Table 1 the number of trainable parameters of each variant. I find it a bit confusing to refer to the setting in which W is learned as ALISTA, as to me ALISTA implies using analytical W. This is clear later in the text (and makes sense from a computational standpoint). Would be good to clarify it early in the text. Finally the paper presents a proof of exponential convergence for ALISTA in the noisy case. While this is an interesting result, it is not very closely linked to the main focus of the work.
assistant
The paper proposes a network architecture composed of three interpretable components followed by a simple MLP classifier. It first applies a scattering transform followed by a learned linear projection (to reduce dimensionality). A sparse representation of these coefficients is then obtained using dictionary learning. The projection, dictionary and MLP classifier are jointly trained to minimize the classification loss. Results show that the model outperforms AlexNet on the Imagenet benchmark. The paper is well written. The main contribution of the work is to present an architecture with composed of mathematically interpretable components achieving very high empirical performance. I find that these results are very significant. The second contribution is to propose a dictionary learning algorithm that uses ISTC and can be trained with gradient descent. I think that it would be interesting to add an ablation showing the change in performance by changing N (the number of iterations in the ISTC net). Also, it would make sense to run FISTA or ISTA unrolls to see if the benefits of the faster convergence also affect classification performance. It would be good to add to Table 1 the number of trainable parameters of each variant. I find it a bit confusing to refer to the setting in which W is learned as ALISTA, as to me ALISTA implies using analytical W. This is clear later in the text (and makes sense from a computational standpoint). Would be good to clarify it early in the text. Finally the paper presents a proof of exponential convergence for ALISTA in the noisy case. While this is an interesting result, it is not very closely linked to the main focus of the work.