ICML2019

Breaking Inter-Layer Co-Adaptation
by Classifier Anonymization
Ikuro Sato1
Kohta Ishikawa1
Guoqing Liu1
Masayuki Tanaka2

1 Denso IT Laboratory. Inc., Japan

2 National Institute of Advanced Industrial

Science and Technology, Japan

I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 2019

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Summary first
About what?

How?

Breaking co-adaptation between
feature extractor and classifier.
By classifier anonymization technique.

Theory?

Proved: Features form simple point-like distribution.

In reality?

Point-like property largely confirmed on real datasets.

I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 20192/10

E2E optimization scheme flourishes. Is it always good?
E2E opt.

𝜙⋆, 𝜃 ⋆

1= arg min෍ 𝐿 𝐶𝜃 𝐹𝜙 𝑥
𝜙,𝜃 𝒟 0,𝑡𝑥,𝑡 ∈𝒟

Input

DNN

Feature Ext.

𝐹𝜙 𝑥

𝑥

Classifier𝐶𝜃 𝐹𝜙 𝑥

Loss w/ target 𝑡

𝐿 𝐶𝜃 𝐹𝜙 𝑥,𝑡Toy ex.)
2-class regression

Feature dim-2

Feature extractor 𝐹𝜙⋆ adapts to a particular classifier 𝐶𝜃 .
‘+1’
‘-1’

color: 𝐶𝜃 value

Features may form
excessively complex distribution.
• Disjointed• Split

Feature dim-1
I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 20193/10

FOCA: Feature-extractor Optimization through Classifier Anonymization
FOCA𝜙 ⋆ = arg min𝜙1
෍ 𝔼𝜃~Θ𝜙 𝐿 𝐶𝜃 𝐹𝜙 𝑥
𝒟 0𝑥,𝑡 ∈𝒟

Random weak classifier: 𝜃~Θ𝜙,𝑡Want to know
more about 𝛩𝜙 ?
Please come tothe poster!

Feature dim-2

Feature extractor 𝐹𝜙⋆ adapts to a set of weak classifiers 𝐶𝜃 .
Features form simple
point-like distribution
per class under someconditions.
Feature dim-1I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 2019

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Proposition about the point-like property
In words,
If feature extractor has an enough representation ability,
all input data of the same class are projected to a single pointin the feature space in a class-separable way undercertain conditions.
Please see
the paperfor the proof.

I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 20195/10

Toy problem demonstration
startx-axis
y-axisFeature dim. #1
Feature dim. #2
data used to generate classifier
decision boundary

Small-batch classifier
works as a weak classifierto the entire dataset.
Small perturbations lead toend

point-like distribution.

I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 2019

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Experiment #1: partial-dataset training
Thing we wish to confirm:
full-dataset classifier

Do they
perform
similarly
for given 𝐹𝜙⋆??

partial-dataset classifier

I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 2019

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Experiment #1: partial-dataset training
CIFAR10 test error rates
Performance gaplarge forother methods

much smaller
for FOCA

classifier trainedwith large dataset

One indication ofpoint-like property

classifier trainedwith small dataset(The same, fixed feature extractor is used within each method.)
I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 2019

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More experiments …
including:• Approximate geodesic distance measurements
between large- and small-dataset solutions• Low-dimensional analyses

to further study the point-like property.

I. Sato, et al., Breaking Inter-Layer Co-Adaptation by Classifier Anonymization, ICML 2019

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Poster #28 tonight
What? Breaking co-adaptation between
feature extractor and classifier.
How? By classifier anonymization.

Theory? Proved: Features form simple
point-like distribution.
Reality? Point-like property largely
confirmed on real datasets.

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