提出新型参数化激活函数ReCA，显著提升深度网络性能。
ReCA: A Parametric ReLU Composite Activation Function
- 基于ReLU设计可学习参数的复合激活函数
- 在多个主流数据集和架构上超越所有基线模型
- 适合追求高性能的深度学习研究与应用
激活函数对深度神经网络性能有显著影响。尽管修正线性单元（ReLU）仍是实际应用中的主流选择，但最优激活函数仍是开放问题。本文提出一种基于ReLU的新型参数化激活函数ReCA，其在多种复杂神经网络架构下，于多个前沿数据集上均表现优于所有基线模型。
原文摘要 · Abstract (English)
Activation functions have been shown to affect the performance of deep neural networks significantly. While the Rectified Linear Unit (ReLU) remains the dominant choice in practice, the optimal activation function for deep neural networks remains an open research question. In this paper, we propose a novel parametric activation function, ReCA, based on ReLU, which has been shown to outperform all baselines on state-of-the-art datasets using different complex neural network architectures.
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