Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition q

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. To that end, we start by recalling four key brain-inspired principles that relate to simple vision: modeling whats and wheres separately; including a time component; context dependency; and layer-wise learning. Then, we take these principles and use them to convey an a priori structure to our model that makes the learning problem easier. With that, our model is able to generate such high quality representations for the MNIST data set. We compare the obtained results with similar recent works and verify extremely competitive results.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Although deep learning has achieved success in visual pattern recognition, it is important to study computational principles in the brain to guide unsupervised learning. By applying four brain-inspired principles, our model can generate high-quality representations and achieve competitive results compared to recent works.