Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
The activation function is an important component in Convolutional Neural Networks (CNNs). For instance, recent breakthroughs in Deep Learning can be attributed to the Rectified Linear Unit (ReLU). Another recently proposed activation function, the Exponential Linear Unit (ELU), has the supplementary property of reducing bias shift without explicitly centering the values at zero. In this paper, we show that learning a parameterization of ELU improves its performance. We analyzed our proposed Parametric ELU (PELU) in the context of vanishing gradients and provide a gradient-based optimization framework. We conducted several experiments on CIFAR-10/100 and ImageNet with different network architectures, such as NiN, Overfeat, All-CNN and ResNet. Our results show that our PELU has relative error improvements over ELU of 4.45% and 5.68% on CIFAR-10 and 100, and as much as 7.28% with only 0.0003% parameter increase on ImageNet. We also observed that Vgg using PELU tended to prefer activations saturating closer to zero, as in ReLU, except at the last layer, which saturated near -2. Finally, other presented results suggest that varying the shape of the activations during training along with the other parameters helps controlling vanishing gradients and bias shift, thus facilitating learning.
Authors present a parameterized variant of ELU and show that the proposed function helps to deal with vanishing gradients in deep networks in a way better than existing non-linearities. They present both a theoretical analysis and practical validation for presented approach. Interesting observations on statistics of the PELU parameters are reported. Perhaps explanation for the observed evolution of parameters can help better understand the non-linearity. It is hard to evaluate the experimental validation presented given the difference in number of parameters compared to other approaches.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
The activation function is an important component in Convolutional Neural Networks (CNNs). For instance, recent breakthroughs in Deep Learning can be attributed to the Rectified Linear Unit (ReLU). Another recently proposed activation function, the Exponential Linear Unit (ELU), has the supplementary property of reducing bias shift without explicitly centering the values at zero. In this paper, we show that learning a parameterization of ELU improves its performance. We analyzed our proposed Parametric ELU (PELU) in the context of vanishing gradients and provide a gradient-based optimization framework. We conducted several experiments on CIFAR-10/100 and ImageNet with different network architectures, such as NiN, Overfeat, All-CNN and ResNet. Our results show that our PELU has relative error improvements over ELU of 4.45% and 5.68% on CIFAR-10 and 100, and as much as 7.28% with only 0.0003% parameter increase on ImageNet. We also observed that Vgg using PELU tended to prefer activations saturating closer to zero, as in ReLU, except at the last layer, which saturated near -2. Finally, other presented results suggest that varying the shape of the activations during training along with the other parameters helps controlling vanishing gradients and bias shift, thus facilitating learning.
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<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>



Authors present a parameterized variant of ELU and show that the proposed function helps to deal with vanishing gradients in deep networks in a way better than existing non-linearities. They present both a theoretical analysis and practical validation for presented approach. Interesting observations on statistics of the PELU parameters are reported. Perhaps explanation for the observed evolution of parameters can help better understand the non-linearity. It is hard to evaluate the experimental validation presented given the difference in number of parameters compared to other approaches.
assistant
Authors present a parameterized variant of ELU and show that the proposed function helps to deal with vanishing gradients in deep networks in a way better than existing non-linearities. They present both a theoretical analysis and practical validation for presented approach. Interesting observations on statistics of the PELU parameters are reported. Perhaps explanation for the observed evolution of parameters can help better understand the non-linearity. It is hard to evaluate the experimental validation presented given the difference in number of parameters compared to other approaches.