title: Introducing Active Learning for CNN under the light of Variational Inference
abstract: One main concern of the deep learning community is to increase the capacity of
  representation of deep networks by increasing their depth. This requires to scale
  up the size of the training database accordingly. Indeed a major intuition lies in
  the fact that the depth of the network and the size of the training set are strongly
  correlated. However recent works tend to show that deep learning may be handled
  with smaller dataset as long as the training samples are carefully selected (let us
  mention for instance curriculum learning). In this context we introduce a scalable
  and efficient active learning method that can be applied to most neural networks,
  especially Convolutional Neural Networks (CNN). To the best of our knowledge,
  this paper is the first of its kind to design an active learning selection scheme based
  on a variational inference for neural networks. We also deduced a formulation of
  the posterior and prior distributions of the weights using statistical knowledge on
  the Maximum Likelihood Estimator.
  We describe our strategy to come up with our active learning criterion. We assess its
  consistency by checking the accuracy obtained by successive active learning steps
  on two benchmark datasets MNIST and USPS. We also demonstrate its scalability
  towards increasing training set size.
authors: Melanie Ducoffe Frederic Precioso
hIndex: Frederic Precioso 19