Deep learning algorithm with visual impression

In this article, we develop two visual impression models: recognition model and generalization model to simulate the cognition process of human visual systems. We show how the visual impression learned with a deep neural network can be efficiently transferred to other visual recognition tasks. By reusing the hidden layers trained in an unsupervised way, we show that we can largely reduce the number of annotated image samples in the target tasks. Experiments show that parameters estimated in the source task can indeed help the network to improve results for object classification in the target tasks.