title: Learning viewpoint invariant object representations using a temporal coherence principle.
abstract: Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability" objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an--also unlabeled--distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations.
authors: Institute of Neuroinformatics, University & ETH Zürich, Zürich, Switzerland. wet@klab.caltech.edu
authors: Wolfgang
authors: W
authors: Einhäuser
authors: Jörg
authors: J
authors: Hipp
authors: Julian
authors: J
authors: Eggert
authors: Edgar
authors: E
authors: Körner
authors: Peter
authors: P
authors: König
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000704:Analysis of Variance; D000818:Animals; D016000:Cluster Analysis; D005793:Generalization, Psychological; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000145:classification / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods; D014793:Visual Cortex / Q000166:cytology* / Q000502:physiology