  We propose to learn a curriculum or a syllabus for supervised learning and
deep reinforcement learning with deep neural networks by an attachable deep
neural network, called ScreenerNet. Specifically, we learn a weight for each
sample by jointly training the ScreenerNet and the main network in an
end-to-end self-paced fashion. The ScreenerNet neither has sampling bias nor
requires to remember the past learning history. We show the networks augmented
with the ScreenerNet achieve early convergence with better accuracy than the
state-of-the-art curricular learning methods in extensive experiments using
three popular vision datasets such as MNIST, CIFAR10 and Pascal VOC2012, and a
Cart-pole task using Deep Q-learning. Moreover, the ScreenerNet can extend
other curriculum learning methods such as Prioritized Experience Replay (PER)
for further accuracy improvement.
