1 research outputs found
Measuring the Data Efficiency of Deep Learning Methods
In this paper, we propose a new experimental protocol and use it to benchmark
the data efficiency --- performance as a function of training set size --- of
two deep learning algorithms, convolutional neural networks (CNNs) and
hierarchical information-preserving graph-based slow feature analysis (HiGSFA),
for tasks in classification and transfer learning scenarios. The algorithms are
trained on different-sized subsets of the MNIST and Omniglot data sets. HiGSFA
outperforms standard CNN networks when the models are trained on 50 and 200
samples per class for MNIST classification. In other cases, the CNNs perform
better. The results suggest that there are cases where greedy, locally optimal
bottom-up learning is equally or more powerful than global gradient-based
learning.Comment: 8 page