invariant object recognition - visnet
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Computational models of invariant object recognitionSupervisor: Dr. Reza EbrahimpourStudent: Alireza Akhavan Pour
HMAX VisNet
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VIEW ON THE VISUAL PATHWAY
Visual Areas of the Human Cerebral Cortex5/31
On center
No lightLight onNo light
No lightLight onNo light
Off center
Neurons and areas in visual system 14/40VIEW ON THE VISUAL PATHWAY
BEHAVIOR OF AN GANGLION CELL 6/31
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No lightwrong position-wrong orientationwright position-wrong orientationwrong position-wright orientationWright positionWright orientation
VIEW ON THE VISUAL PATHWAY, SIMPLE CORTICAL CELLS7/31
Behavior of a complex cell 8/31
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9 HMAX VisNet
Hmax Hubel Wiesel Hubel Wiesel :
( receptive field) : (feed-forward)
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4 11/31V1-V2
V1-V2
V2-V4
V4/PIT
ITS1
C1
S2
C2
12 HMAX VisNet
13/31 :VisNet
Continuous transformation (CT) learning
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:VisNet15/31
VisNet 16/31Edmund T. Rolls
4 (local graded inhibition) (topologicall) (modified Hebbian learning rule )V2-> V4->posterior inferior temporal -> anterior inferior temporal
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67%
128*128
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Architecture17/31
1 V1
input18/31
Input to layer 1
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I .
4- .
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4 3 2 191889899.2267540190
contrast enhancement (contrast enhancement) 0 1
:r ( ) : y : ( !) : activation
sparseness21/31
(trace learning rule) :
0.5 .22/31
xj j- y t y t wj j- trace value ( )
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Neural-like models via performance optimizationD. . L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert and J. J. DiCarlo, "Performance-optimized hierarchical models predict neural responses in higher visual cortex," Proceedings of the National Academy of Sciences, p. 201403112, 2014. 25/31
VisNet Hmax
VisnetHmaxtesttesttraintrain26/31
27 HMAX VisNet
invariency (re-train) GPU Spike 28/31
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29References
E. Rolls, " Invariant visual object and face recognition: neural and computational bases, and a model," In Computational Neuroscience, vol. 51, pp. 167194, 2012.
T. Rolls, E.T., and Stringer, "Continuous transformation learning of translation invariant representations," Exp.BainRes, pp. 255270, 2010.
D. L. K. Yamins, H. Hong, C. F. Cadieu, E. A. Solomon, D. Seibert and J. J. DiCarlo, "Performance-optimized hierarchical models predict neural responses in higher visual cortex," Proceedings of the National Academy of Sciences, p. 201403112, 2014.
M. Riesenhuber and T. Poggio, "Hierarchical Models of Object Recognition in Cortex," Nature Neuroscience, vol. 2, no. 11, pp. 1019-1025, 1999.
Stringer, S. M. and E. T. Rolls. 2008. Learning transform invariant object recognition in the visual system withmultiple stimuli present during training, Neural Networks 21:888-903
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[1] E. a. T. Rolls, " Processing speed in the cerebral cortex and the neurophysiology of visual masking. Proc.R.Soc.Lond.BBiol.Sci. 257, 915.," 1994.
[2] S. S. P. a. N. R. Hestrin, "Mechanisms generating the time course of dual component exci tatory synaptic currents recorded in hippocampal slices.," Neuron 5, 1990
[3] P. G. J. a. E. m. G. Montague, "Spatial signalling in the development and function of neural connections," Cereb.Cortex 1, 199220.REFERENCES31/31