| Summary: | Curriculum learning is a machine learning technique inspired by the way humans acquire knowledge and skills: by mastering simple concepts first, and progressing through information with increasing difficulty to grasp more complex topics. Curriculum Learning, and its derivatives Self Paced Learning (SPL) and Self Paced Learning with Diversity (SPLD), have been previously applied within various machine learning contexts: Support Vector Machines (SVMs), perceptrons, and multi-layer neural networks, where they have been shown to improve both training speed and model accuracy. This project ventured to apply the techniques within the previously unexplored context of deep learning, by investigating how they affect the performance of a deep convolutional neural network (ConvNet) trained on a large labeled image dataset. The curriculum was formed by presenting the training samples to the network in order of increasing difficulty, measured by the sample's loss value based on the network's objective function. The project evaluated SPL and SPLD, and proposed two new curriculum learning sub-variants, p-SPL and p-SPLD, which allow for a smooth progresson of sample inclusion during training. The project also explored the "inversed" versions of the SPL, SPLD, p-SPL and p-SPLD techniques, where the samples were selected for the curriculum in order of decreasing difficulty. The experiments demonstrated that all learning variants perform fairly similarly, within ≈1% average test accuracy margin, based on five trained models per variant. Surprisingly, models trained with the inversed version of the algorithms performed slightly better than the standard curriculum training variants. The SPLD-Inversed, SPL-Inversed and SPLD networks also registered marginally higher accuracy results than the network trained with the usual random sample presentation. The results suggest that while sample ordering does affect the training process, the optimal order in which samples are presented may vary based on the data set and algorithm used. The project also investigated whether some samples were more beneficial for the training process than others. Based on sample difficulty, subsets of samples were removed from the training data set. The models trained on the remaining samples were compared to a default model trained on all samples. On the data set used, removing the “easiest” 10% of samples had no effect on the achieved test accuracy compared to the default model, and removing the “easiest” 40% of samples reduced model accuracy by only ≈1% (compared to ≈6% loss when 40% of the "most difficult" samples were removed, and ≈3% loss when 40% of samples were randomly removed). Taking away the "easiest" samples first (up to a certain percentage of the data set) affected the learning process less negatively than removing random samples, while removing the "most difficult" samples first had the most detrimental effect. The results suggest that the networks derived most learning value from the "difficult" samples, and that a large subset of the "easiest" samples can be excluded from training with minimal impact on the attained model accuracy. Moreover, it is possible to identify these samples early during training, which can greatly reduce the training time for these models.
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