DeepAI AI Chat
Log In Sign Up

Parametric Exponential Linear Unit for Deep Convolutional Neural Networks

by   Ludovic Trottier, et al.
Université Laval

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 on CIFAR-10 and 100, and as much as 7.28 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.


page 1

page 2

page 3

page 4


Natural-Logarithm-Rectified Activation Function in Convolutional Neural Networks

Activation functions play a key role in providing remarkable performance...

Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks

The Rectified Linear Unit is currently a state-of-the-art activation fun...

EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks

For most state-of-the-art architectures, Rectified Linear Unit (ReLU) be...

Accelerating CNN Training by Sparsifying Activation Gradients

Gradients to activations get involved in most of the calculations during...

A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks

We present a novel algorithm for training deep neural networks in superv...

Self-Normalizing Neural Networks

Deep Learning has revolutionized vision via convolutional neural network...

Training Neural Networks by Using Power Linear Units (PoLUs)

In this paper, we introduce "Power Linear Unit" (PoLU) which increases t...

Code Repositories


Parametric Exponential Linear Unit for ResNet in Torch from

view repo