The paper introduces the Exponential Linear Unit (ELU), a novel activation function that improves learning speed and classification accuracy in deep neural networks. Unlike ReLU and LReLU, ELUs produce negative values, which help bring the mean activation closer to zero, reducing the bias shift effect and accelerating learning. ELUs also provide noise-robust deactivation states, making them more effective in modeling the presence of input features while not quantifying their absence. ELUs outperform ReLU and LReLU in experiments on various datasets, including CIFAR-100 and ImageNet. On CIFAR-100, ELU networks achieve the best published result without using multi-view evaluation or model averaging. On ImageNet, ELU networks learn faster and achieve lower classification error than ReLU networks. The ELU's negative saturation helps reduce the variation and information propagated through the network, leading to more stable and robust representations. The paper also discusses the theoretical basis for ELUs' effectiveness, showing how the unit natural gradient corrects bias shifts by adjusting the interactions between incoming units and the bias unit. This correction brings the gradient closer to the natural gradient, improving learning efficiency. ELUs are shown to be more effective than other activation functions in reducing bias shifts and improving generalization performance. The experiments demonstrate that ELUs significantly outperform other activation functions in terms of learning speed and classification accuracy. ELU networks achieve lower training loss and test error compared to ReLU, LReLU, and SReLU networks. They also perform better than ReLU networks with batch normalization, indicating that ELUs are more effective in reducing bias shifts and improving learning efficiency. The results suggest that ELUs are a promising activation function for deep neural networks, offering faster learning and better generalization performance.The paper introduces the Exponential Linear Unit (ELU), a novel activation function that improves learning speed and classification accuracy in deep neural networks. Unlike ReLU and LReLU, ELUs produce negative values, which help bring the mean activation closer to zero, reducing the bias shift effect and accelerating learning. ELUs also provide noise-robust deactivation states, making them more effective in modeling the presence of input features while not quantifying their absence. ELUs outperform ReLU and LReLU in experiments on various datasets, including CIFAR-100 and ImageNet. On CIFAR-100, ELU networks achieve the best published result without using multi-view evaluation or model averaging. On ImageNet, ELU networks learn faster and achieve lower classification error than ReLU networks. The ELU's negative saturation helps reduce the variation and information propagated through the network, leading to more stable and robust representations. The paper also discusses the theoretical basis for ELUs' effectiveness, showing how the unit natural gradient corrects bias shifts by adjusting the interactions between incoming units and the bias unit. This correction brings the gradient closer to the natural gradient, improving learning efficiency. ELUs are shown to be more effective than other activation functions in reducing bias shifts and improving generalization performance. The experiments demonstrate that ELUs significantly outperform other activation functions in terms of learning speed and classification accuracy. ELU networks achieve lower training loss and test error compared to ReLU, LReLU, and SReLU networks. They also perform better than ReLU networks with batch normalization, indicating that ELUs are more effective in reducing bias shifts and improving learning efficiency. The results suggest that ELUs are a promising activation function for deep neural networks, offering faster learning and better generalization performance.