Visual Perception with Deep Learning

Google Tech Talks
April, 9 2008

ABSTRACT

A long-term goal of Machine Learning research is to solve highy
complex "intelligent" tasks, such as visual perception auditory
perception, and language understanding. To reach that goal, the ML
community must solve two problems: the Deep Learning Problem, and the
Partition Function Problem.

There is considerable theoretical and empirical evidence that complex
tasks, such as invariant object recognition in vision, require "deep"
architectures, composed of multiple layers of trainable non-linear
modules. The Deep Learning Problem is related to the difficulty of
training such deep architectures.

Several methods have recently been proposed to train (or pre-train)
deep architectures in an unsupervised fashion. Each layer of the deep
architecture is composed of an encoder which computes a feature vector
from the input, and a decoder which reconstructs the input from the
features. A large number of such layers can be stacked and trained
sequentially, thereby learning a deep hierarchy of features with
increasing levels of abstraction. The training of each layer can be
seen as shaping an energy landscape with low valleys around the
training samples and high plateaus everywhere else. Forming these
high plateaus constitute the so-called Partition Function problem.

A particular class of methods for deep energy-based unsupervised
learning will be described that solves the Partition Function problem
by imposing sparsity constraints on the features. The method can learn
multiple levels of sparse and overcomplete representations of
data. When applied to natural image patches, the method produces
hierarchies of filters similar to those found in the mammalian visual
cortex.

An application to category-level object recognition with invariance to
pose and illumination will be described (with a live demo). Another
application to vision-based navigation for off-road mobile robots will
be described (with videos). The system autonomously learns to
discriminate obstacles from traversable areas at long range.

This is joint work with Y-Lan Boureau, Sumit Chopra, Raia Hadsell,
Fu-Jie Huang, Koray Kavakcuoglu, and Marc'Aurelio Ranzato.

Speaker: Yann Le Cun
Computational and Biological Learning Lab,
Courant Institute of Mathematical Sciences,
New York University.

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  People & Blogs

[UniverseApproved]

does anyone remember what it was like playing "I spy with my little eye" when they were little?

[UniverseApproved]

Seems an AI-complete computer vision system capable of such high resolution and presicion in it's relative-informational simulations would be highly relevant to controlling finance and governance. This enhanced ability to monitor and manipulate the matter on our planet seems part of a profound process to me.

I was just being silly in my original post, I mean you must have read Baudrillard's On Simulacra and Simulation, eh?

[UniverseApproved]

Consider AI-complete computer vision used as better, more efficient Vingean "localizers". Imagine a system like us (AI complete) but that has perfect memory, can "see" (and to a high degree of accuracy predict) the "numbers" between distinct objects within the visual input from it's physical environment, can see in other than visible light, and that operates millions of times faster than a human being.

[UniverseApproved]

wissner-gross talks about this in his "on planetary-scale intelligence" talk...if you believe that AI is likely to be produced through an economic process, then AI is highly likely to arise in the quantitative finance or quantitative advertising markets. Consider the high speed algorithms involved; better modeling of human behavior allows more money to be made, you know, sifting through entropy uncovering and exploiting hidden information.

[3jrhall]

If you know of something better than Restricted Boltzmann Machines for machine learning, I'd love to hear about it. And what does machine learning have to do with controlling finance and government? That's controlled by human greed, not weighted logic.

[UniverseApproved]

why are all you smart people allowing outdated governance and finance models to rule our realities? Don't you wish for shared freedom? I mean, this is some sad ass modeling.

[CassandraAbbey]

Blasphemy? how come?

[adelle0001]

Good job! ang ganda ng video!

[Steve Grimm]

Does anyone else think the intro guy looks like a young Jeff Goldblum?? ;)