Download - Ò %Ê'2 · InfoGAN #Ý 8 S # ... Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, Xi Chen, Yan Duan, Rein, Houthooft, John Schulman, Ilya

RBM

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● IL-RBM

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AR

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Fig. Incremental Learning RBM

Fig. Fig.

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SLAM

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SLAM

FastSLAM

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● ( μ Σ

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Nonverbal communication system

recognition

What is

the state

of the

class?

Student 1

Student 2

Student 3

What are

the

students

thinking?…

(Suggestion received)

Then, let’s

try to …

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→

U-net VAE

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InfoGAN

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x Z

μ

σ

Pθ(x|z) x’

Encoder Decoder

input output

Encoder Decoder

DecoderEncoder

Z1

Z2

U-VAE

VAE

Z1 Z2

image

question

concat

CN

N Attention

weig

hte

d

concat answer

show ask attend

and answer

Generator

show ask attend

and answer

Decoder

show ask attend

and answer

Discriminator

image

quasi question

z~N(0,I)

fake sentence

real sentence

real or fake

reconstructed

quasi question

z_hat

VQA(Visual Question

Answering)

[1]

[1] Show, Ask, Attend, and Answer: A Strong Baseline For Visual

Question Answering, Vahid Kazemi, Ali Elqursh,

arXiv:1704.03162v2[cs.CV]

InfoGAN[2]

[2] InfoGAN: Interpretable Representation Learning by Information

Maximizing Generative Adversarial Nets, Xi Chen, Yan Duan, Rein,

Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel, arXiv:1606.03657v1 [cs.LG]

VAE U-net

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POI

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1.

2. 3

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→

Conventional SV

Personalized SV

Point Of Interest

( )

image

@ailab.ics.keio.ac.jp

↓

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0.

1.

2.

λ:

Gi:

Gj:UP