  The field of meta-learning has seen a dramatic rise in interest in recent
years. In existing meta-learning approaches, learning tasks for training
meta-models are usually collected from public datasets, which brings the
difficulty of obtaining a sufficient number of meta-learning tasks with a large
amount of training data. In this paper, we propose a meta-learning approach
based on randomly generated meta-learning tasks to obtain a parametric loss for
classification learning based on big data. The loss is represented by a deep
neural network, called meta-loss network (MLN). To train the MLN, we construct
a large number of classification learning tasks through randomly generating
training data, validation data, and corresponding ground-truth linear
classifier. Our approach has two advantages. First, sufficient meta-learning
tasks with large number of training data can be obtained easily. Second, the
ground-truth classifier is given, so that the difference between the learned
classifier and the ground-truth model can be measured to reflect the
performance of MLN more precisely than validation accuracy. Based on this
difference, we apply the evolutionary strategy algorithm to find out the
optimal MLN. The resultant MLN not only leads to satisfactory learning effects
on generated linear classifier learning tasks for testing, but also behaves
very well on generated nonlinear classifier learning tasks and various public
classification tasks. Our MLN stably surpass cross-entropy (CE) and mean square
error (MSE) in testing accuracy and generalization ability. These results
illustrate the possibility of achieving satisfactory meta-learning effects
using generated learning tasks.
