visual deep learning models,cs.wellesley.edu/~vision/slides/tommy_class.pdf · • human brain...

Center Name Presenter Name

Visual deep learning models, in particular for face recognition

and models of invariant recognition

in the ventral stream

Towards a theory of the above

tomaso poggio, CBMM, BCS, CSAIL, McGovern MIT

Plan

• Recognition in visual cortex • DCLNs • Deep Face systems • iTheory

Second  Annual  NSF  Site  Visit,  June  2  –  3,  2015

Theoretical/conceptual framework for vision

• The first 100ms of vision: feedforward and invariant: what, who, where

• Top-down needed for verification step and more complex questions: generative models, probabilistic inference, top-down visual routines.

Following this conceptual framework we are working on:

1.a theory of invariance cortical computation —> i-theory2.a generative approach, probabilistic in nature 3.visual routines, and of how they may be learned.

Object  recogni-on

• Human Brain –1010-1011 neurons (~1 million flies) –1014- 1015 synapses

Vision:  what  is  where  

• Ventral stream in rhesus monkey –~109 neurons in the ventral stream

(350 106 in each emisphere) –~15 106 neurons in AIT (Anterior

InferoTemporal) cortex

• ~200M in V1, ~200M in V2, 50M in V4

Van Essen & Anderson, 1990

Source: Lennie, Maunsell, Movshon

Vision:  what  is  where  

[software available online]Riesenhuber & Poggio 1999, 2000; Serre Kouh Cadieu Knoblich Kreiman & Poggio 2005; Serre Oliva Poggio 2007

• It is in the family of “Hubel-Wiesel” models (Hubel & Wiesel, 1959: qual. Fukushima, 1980: quant; Oram & Perrett, 1993: qual; Wallis & Rolls, 1997; Riesenhuber & Poggio, 1999; Thorpe, 2002; Ullman et al., 2002; Mel, 1997; Wersing and Koerner, 2003; LeCun et al 1998: not-bio; Amit & Mascaro, 2003: not-bio; Hinton, LeCun, Bengio not-bio; Deco & Rolls 2006…)

• As a biological model of object recognition in the ventral stream – from V1 to PFC -- it is perhaps the most quantitatively faithful to known neuroscience data

   Recogni-on  in  Visual  Cortex:  ‘’classical  model”,  selec-ve  and  invariant

Feedforward Models: “predict” rapid categorization (82% model vs. 80% humans)

Hierarchical feedforward models of the ventral stream

Why do these networks including DLCNs

work so well?

Models are not enough… we need a theory!

Plan

• Recognition in visual cortex • DCLNs • Deep Face systems • iTheory

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Invariance via pooling

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New name for virtual examples

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A poor man regularization!

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Mobileye

Plan

• Recognition in visual cortex • DCLNs • Deep Face systems • iTheory

Plan

• Recognition in visual cortex • DCLNs • Deep Face systems • iTheory

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i-theoryLearning of invariant&selective Representations in Sensory Cortex

i-theory: exploring a new hypothesis

A main computational goal of the feedforward ventral stream hierarchy — and of vision — is to compute a representation for each incoming image which is invariant to transformations previously experienced in the visual environment.

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Empirical  demonstraCon:  invariant  representaCon  leads  to  lower  sample  complexity  for  a  supervised  classifier

Theorem   (transla)on   case)  Consider   a   space   of   images   of  dimensions                            pixels  which   may   appear   in   any  posiCon   within   a   window   of  size                      pixels.  The  usual  image   representaCon   yields   a  sample  complexity  (  of  a  linear  c l a s s i fi e r )     o f  order                                ;the    oracle  representaCon     (invariant)  yields   (because   of   much  smaller   covering   numbers)   a    sample  complexity  of  order

d × d

rd × rd

m = O(r2d 2 )

moracle = O(d2 ) =

mimage

r2

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An algorithm that learns in an unsupervised way to compute invariant representations

ν

P(ν )

νµkn(I) = 1/|G|

|G|X

i=1

�(I · gitk + n�)

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Invariant signature from a single image of a new object

We need only a finite number of projections, K, to distinguish among n images.

Similar in spirit to Johnson-Lindestrauss

Local and global invariance: whole-parts theorem

l=4

l=3

l=2

l=1HW module

biophysics: prediction

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Basic machine: a HW module (dot products and histograms/moments for image seen through RF)

• The cumulative histogram (empirical cdf) can be be computed as

• This maps directly into a set of simple cells with threshold

• …and a complex cell indexed by n and k summating the simple cells

µnk (I ) = 1

|G |σ ( I ,git

k + nΔ)i=1

|G |

∑

nΔ

The nonlinearity can be rather arbitrary for invariance provided it is stationary in time

Second  Annual  NSF  Site  Visit,  June  2  –  3,  2015

Dendrites of a complex cells as simple cells…

Active properties in the dendrites of the complex cell

Plan

• i-theory • DCLNs • equivalence to DCLNs, theory notes • Some predictions of i-theory • Deep Face systems