Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
A fast learning algorithm for deep belief nets
Neural Computation
Maximal Causes for Non-linear Component Extraction
The Journal of Machine Learning Research
Rapid convergence to feature layer correspondences
Neural Computation
A Visual Object Recognition System Invariant to Scale and Rotation
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning of Neural Information Routing for Correspondence Finding
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A parallel computation that assigns canonical object-based frames of reference
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Dynamics of cortical columns – self-organization of receptive fields
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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We study an artificial neural network that learns the invariance properties of objects from data. We start with a bag-of-features encoding of a specific object and repeatedly show the object in different transformations. The network then learns unsupervised from the data what the possible transformations are and what feature arrangements are typical for the object shown. The information about transformations and feature arrangements is hereby represented by a lateral network of excitatory connections among units that control the information exchange between an input and a down-stream neural layer. We build up on earlier work in this direction that kept a close relation to novel anatomical and physiological data on the cortical architecture and on its information processing and learning. At the same time we show, based on a new synaptic plasticity rules, that learning results in a strong increase of object finding rates in both artificial and more realistic experiments.