  Research in Curriculum Learning has shown better performance on the task by
optimizing the sequence of the training data. Recent works have focused on
using complex reinforcement learning techniques to find the optimal data
ordering strategy to maximize learning for a given network. In this paper, we
present a simple yet efficient technique based on continuous optimization
trained with auto-encoding procedure. We call this new approach Training
Sequence Optimization (TSO). With a usual encoder-decoder setup we try to learn
the latent space continuous representation of the training strategy and a
predictor network is used on the continuous representation to predict the
accuracy of the strategy on the fixed network architecture. The performance
predictor and encoder enable us to perform gradient-based optimization by
gradually moving towards the latent space representation of training data
ordering with potentially better accuracy. We show an empirical gain of 2AP
with our generated optimal curriculum strategy over the random strategy using
the CIFAR-100 and CIFAR-10 datasets and have better boosts than the existing
state-of-the-art CL algorithms.

Machine Learning;Artificial Intelligence