  In this paper we investigate the performance of different types of rectified
activation functions in convolutional neural network: standard rectified linear
unit (ReLU), leaky rectified linear unit (Leaky ReLU), parametric rectified
linear unit (PReLU) and a new randomized leaky rectified linear units (RReLU).
We evaluate these activation function on standard image classification task.
Our experiments suggest that incorporating a non-zero slope for negative part
in rectified activation units could consistently improve the results. Thus our
findings are negative on the common belief that sparsity is the key of good
performance in ReLU. Moreover, on small scale dataset, using deterministic
negative slope or learning it are both prone to overfitting. They are not as
effective as using their randomized counterpart. By using RReLU, we achieved
75.68\% accuracy on CIFAR-100 test set without multiple test or ensemble.
