Energy-Based Spherical Sparse Coding
In this paper, we explore an efficient variant of convolutional sparse coding with unit norm code vectors and reconstructions are evaluated using an inner product (cosine distance). To use these codes for discriminative classification, we describe a model we term Energy-Based Spherical Sparse Coding (EB-SSC) in which the hypothesized class label introduces a learned linear bias into the coding step. We evaluate and visualize performance of stacking this encoder to make a deep layered model for image classification.
The paper introduces an efficient variant of sparse coding and uses it as a building block in CNNs for image classification. The coding method incorporates both the input signal reconstruction objective as well as top down information from a class label. The proposed block is evaluated against the recently proposed CReLU activation block. Positives: The proposed method seems technically sound, and it introduces a new way to efficiently train a CNN layer-wise by combining reconstruction and discriminative objectives. Negatives: The performance gain (in terms of classification accuracy) over the previous state-of-the-art is not clear. Using only one dataset (CIFAR-10), the proposed method performs slightly better than the CRelu baseline, but the improvement is quite small (0.5% in the test set). The paper can be strengthened if the authors can demonstrate that the proposed method can be generally applicable to various CNN architectures and datasets with clear and consistent performance gains over strong CNN baselines. Without such results, the practical significance of this work seems unclear.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Energy-Based Spherical Sparse Coding
In this paper, we explore an efficient variant of convolutional sparse coding with unit norm code vectors and reconstructions are evaluated using an inner product (cosine distance). To use these codes for discriminative classification, we describe a model we term Energy-Based Spherical Sparse Coding (EB-SSC) in which the hypothesized class label introduces a learned linear bias into the coding step. We evaluate and visualize performance of stacking this encoder to make a deep layered model for image classification.
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 introduces an efficient variant of sparse coding and uses it as a building block in CNNs for image classification. The coding method incorporates both the input signal reconstruction objective as well as top down information from a class label. The proposed block is evaluated against the recently proposed CReLU activation block. Positives: The proposed method seems technically sound, and it introduces a new way to efficiently train a CNN layer-wise by combining reconstruction and discriminative objectives. Negatives: The performance gain (in terms of classification accuracy) over the previous state-of-the-art is not clear. Using only one dataset (CIFAR-10), the proposed method performs slightly better than the CRelu baseline, but the improvement is quite small (0.5% in the test set). The paper can be strengthened if the authors can demonstrate that the proposed method can be generally applicable to various CNN architectures and datasets with clear and consistent performance gains over strong CNN baselines. Without such results, the practical significance of this work seems unclear.
assistant
The paper introduces an efficient variant of sparse coding and uses it as a building block in CNNs for image classification. The coding method incorporates both the input signal reconstruction objective as well as top down information from a class label. The proposed block is evaluated against the recently proposed CReLU activation block. Positives: The proposed method seems technically sound, and it introduces a new way to efficiently train a CNN layer-wise by combining reconstruction and discriminative objectives. Negatives: The performance gain (in terms of classification accuracy) over the previous state-of-the-art is not clear. Using only one dataset (CIFAR-10), the proposed method performs slightly better than the CRelu baseline, but the improvement is quite small (0.5% in the test set). The paper can be strengthened if the authors can demonstrate that the proposed method can be generally applicable to various CNN architectures and datasets with clear and consistent performance gains over strong CNN baselines. Without such results, the practical significance of this work seems unclear.