Invariance of object detection in untrained deep neural networks.

ABSTRACT: The ability to perceive visual objects with various types of transformations, such as rotation, translation, and scaling, is crucial for consistent object recognition. In machine learning, invariant object detection for a network is often implemented by augmentation with a massive number of training images, but the mechanism of invariant object detection in biological brains-how invariance arises initially and whether it requires visual experience-remains elusive. Here, using a model neural network of the hierarchical visual pathway of the brain, we show that invariance of object detection can emerge spontaneously in the complete absence of learning. First, we found that units selective to a particular object class arise in randomly initialized networks even before visual training. Intrigu

SUBMITTER: Cheon J 

PROVIDER: S-EPMC9669311 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature