Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition
This work addresses the challenge of learning from limited labeled data for visual tasks, though it appears incremental as it builds on existing brain-inspired approaches.
The authors tackled the problem of unsupervised learning for visual pattern recognition by exploring brain-inspired principles, achieving extremely competitive results on the MNIST dataset.
Although deep learning has solved difficult problems in visual pattern recognition, it is mostly successful in tasks where there are lots of labeled training data available. Furthermore, the global back-propagation based training rule and the amount of employed layers represents a departure from biological inspiration. The brain is able to perform most of these tasks in a very general way from limited to no labeled data. For these reasons it is still a key research question to look into computational principles in the brain that can help guide models to unsupervisedly learn good representations which can then be used to perform tasks like classification. In this work we explore some of these principles to generate such representations for the MNIST data set. We compare the obtained results with similar recent works and verify extremely competitive results.