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The Moving Picture Experts Group

Coerces the argument to by calling the Bool method on it, and returns the negation of the result. Note that this collapses Junction s.

multi sub prefix : < + > ( Any --> Numeric : D )

Numeric context operator .

Coerces the argument to Numeric by calling the Numeric method on it.

multi sub prefix : < - > ( Any --> Numeric : D )

Negative numeric context operator .

Coerces the argument to Numeric by calling the Numeric method on it, and then negates the result.

multi sub prefix : < ~ > ( Any --> Str : D )

String context operator .

Coerces the argument to by calling the method on it.

Flattens objects of type Capture , , , and into an argument list.

sub slurpee ( | args ) {
say args . perl
slurpee ( < abcd > , { e => 3 } , ' e ' => ' f ' => 33 )
# OUTPUT:«\(("a","b","c","d"),{:e(3)},:e(:f(33)))␤»

Please see the Signature page, specially the section on Captures for more information on the subject.

How neural networks build up their understanding of images

Edges (layer conv2d0)
Textures (layer mixed3a)
Patterns (layer mixed4a)
Parts (layers mixed4b mixed4c)
Objects (layers mixed4d mixed4e)

Authors

Affiliations

Chris Olah

Google Brain Team

Alexander Mordvintsev

Google Research

Ludwig Schubert

Google Brain Team

Published

Nov. 7, 2017

DOI

10.23915/distill.00007

There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution.

Feature visualization
Attribution

This article focuses on feature visualization. While feature visualization is a powerful tool, actually getting it to work involves a number of details. In this article, we examine the major issues and explore common approaches to solving them. We find that remarkably simple methods can produce high-quality visualizations. Along the way we introduce a few tricks for exploring variation in what neurons react to, how they interact, and how to improve the optimization process.

Neural networks are, generally speaking, differentiable with respect to their inputs. If we want to find out what kind of input would cause a certain behavior — whether that’s an internal neuron firing or the final output behavior — we can use derivatives to iteratively tweak the input towards that goal .

While conceptually simple, there are subtle challenges in getting the optimization to work. We will explore them, as well as common approaches to tackle them in the section ” The Enemy of Feature Visualization ″.

What do we want examples of? This is the core question in working with examples, regardless of whether we’re searching through a dataset to find the examples, or optimizing images to create them from scratch. We have a wide variety of options in what we search for:

Different optimization objectives show what different parts of a network are looking for.

optimization objectives

n layer index x,y spatial position z channel index k class index

Neuron
Channel

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