Local Critic Training of Deep Neural Networks
This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.
 This paper proposes an alternative training paradigm for DNIs: instead of training an auxiliary module to approximate the gradient provided by the following modules of the original model, they train it to approximate directly the final output of the original model. Although the approach does not seem to improve significantly, if at all, over Sobolev training of these modules (denoted *critic* in the paper), this method seems simpler and offer side benefits which seem to be the main contribution of this paper. Indeed Table 4 and 5 show for example how they can, after training the full model, extract a submodel requiring significantly less computation and parameters with little loss in the performance. Alternatively, they also propose a method to do progressive inference for similar reasons. The ensembling result is interesting, but the figure 4 is not necessarily clear on the significance of this result, especially since the standard deviation is not shown in this figure. The idea proposed in this paper is interesting, however, the experiments are restricted on relatively small and simple architectures and limited on two very similar datasets (CIFAR-10 and CIFAR-100), making the argument less compelling.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Local Critic Training of Deep Neural Networks
This paper proposes a novel approach to train deep neural networks by unlocking the layer-wise dependency of backpropagation training. The approach employs additional modules called local critic networks besides the main network model to be trained, which are used to obtain error gradients without complete feedforward and backward propagation processes. We propose a cascaded learning strategy for these local networks. In addition, the approach is also useful from multi-model perspectives, including structural optimization of neural networks, computationally efficient progressive inference, and ensemble classification for performance improvement. Experimental results show the effectiveness of the proposed approach and suggest guidelines for determining appropriate algorithm parameters.
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 an alternative training paradigm for DNIs: instead of training an auxiliary module to approximate the gradient provided by the following modules of the original model, they train it to approximate directly the final output of the original model. Although the approach does not seem to improve significantly, if at all, over Sobolev training of these modules (denoted *critic* in the paper), this method seems simpler and offer side benefits which seem to be the main contribution of this paper. Indeed Table 4 and 5 show for example how they can, after training the full model, extract a submodel requiring significantly less computation and parameters with little loss in the performance. Alternatively, they also propose a method to do progressive inference for similar reasons. The ensembling result is interesting, but the figure 4 is not necessarily clear on the significance of this result, especially since the standard deviation is not shown in this figure. The idea proposed in this paper is interesting, however, the experiments are restricted on relatively small and simple architectures and limited on two very similar datasets (CIFAR-10 and CIFAR-100), making the argument less compelling.
assistant
 This paper proposes an alternative training paradigm for DNIs: instead of training an auxiliary module to approximate the gradient provided by the following modules of the original model, they train it to approximate directly the final output of the original model. Although the approach does not seem to improve significantly, if at all, over Sobolev training of these modules (denoted *critic* in the paper), this method seems simpler and offer side benefits which seem to be the main contribution of this paper. Indeed Table 4 and 5 show for example how they can, after training the full model, extract a submodel requiring significantly less computation and parameters with little loss in the performance. Alternatively, they also propose a method to do progressive inference for similar reasons. The ensembling result is interesting, but the figure 4 is not necessarily clear on the significance of this result, especially since the standard deviation is not shown in this figure. The idea proposed in this paper is interesting, however, the experiments are restricted on relatively small and simple architectures and limited on two very similar datasets (CIFAR-10 and CIFAR-100), making the argument less compelling.