Learning Robust Object Recognition Using Composed Scenes from Generative Models

Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.Comment: Accepted by 14th Conference on Computer and Robot Visio

Recognizing Investment Opportunities at the Onset of Recoveries

Investment decision-making is modeled by means of a Kohonen neural net, where neurons represent firms. This is done in order to model investments in novel fields of economic activity, that according to this model are carried out when firms recognize the emergence of a new technological pattern. Combination of the equations of Kohonen model neuron with macroeconomic relationships yields disaggregated accelerator equations with flexible coefficients, that in the aggregate and fixed- coefficients case boil down to traditional accelerator equations. A simulation tests the model in a situation that is remindful of the very beginning of economic recoveries.Accelerator, Investment, Neural Nets, Cognition