Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

11/23/2015
by   Djork-Arné Clevert, et al.
0

We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise-robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR-100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR-10 results and yield the best published result on CIFAR-100, without resorting to multi-view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10 classification error for a single crop, single model network.

READ FULL TEXT

page 7

page 8

research
04/14/2016

Deep Residual Networks with Exponential Linear Unit

Very deep convolutional neural networks introduced new problems like van...
research
07/13/2017

Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting

In this work, we show that saturating output activation functions, such ...
research
02/01/2018

Training Neural Networks by Using Power Linear Units (PoLUs)

In this paper, we introduce "Power Linear Unit" (PoLU) which increases t...
research
04/23/2023

The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations

Deep neural networks based on batch normalization and ReLU-like activati...
research
01/09/2017

QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures

We present QuickNet, a fast and accurate network architecture that is bo...
research
06/08/2017

Self-Normalizing Neural Networks

Deep Learning has revolutionized vision via convolutional neural network...
research
11/09/2015

Batch-normalized Maxout Network in Network

This paper reports a novel deep architecture referred to as Maxout netwo...

Please sign up or login with your details

Forgot password? Click here to reset