Ò %Ê'2 · InfoGAN #Ý 8 S # ... Interpretable Representation Learning by Information Maximizing...

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ࢸࢡ ࢸࢫࢩࢺ RBM 㐃⤡ඛ㸸[email protected] 㐃⤡ඛ㸸[email protected] ヰ୰ே㛫⯆ࡢࡢࡁ⯆ࡢ ᥎ᐃࢸࢫࢩ㛤Ⓨ ⮬ᚊⓗ┦ᡭሙᡤㆡ 㐲㝸᧯స࠸ࡋᐇ⌧ Ꮫ⩦㏵୰⣲Ꮚ㏲ḟⓗ㏣ຍᡭἲ IL-RBM༢ᒙ✵㛫ⓗ␗ᢳ㇟ᗘ ≉ᚩ㔞⋓ᚓ 㐃⤡ඛ㸸[email protected] AR ࡘ࠸ Ỵᐃᑟධ ࡓ࠸ 㐣ཤᑐヰᒚṔඃඛᗘὀ┠ࡓࡋࢫࢡᑐヰ 㐃⤡ඛ㸸[email protected] ࡋࡊ ᨃே࡞ࠎሗᩍࡣࡕFig. Incremental Learning RBM Fig. ど⤖ᯝẚ㍑ Fig. Ⓨⅆ㢖ᗘẚ㍑

Transcript of Ò %Ê'2 · InfoGAN #Ý 8 S # ... Interpretable Representation Learning by Information Maximizing...

Page 1: 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

[email protected] [email protected]

● IL-RBM

[email protected]

AR

[email protected]

Fig. Incremental Learning RBM

Fig. Fig.

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[email protected]

[email protected]

SLAM

[email protected]

SLAM

FastSLAM

● ( μ Σ

)

[email protected]

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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[email protected]

[email protected]

U-net VAE

[email protected]

InfoGAN

[email protected]

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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[email protected]

POI

[email protected]

1.

2. 3

[email protected]

Conventional SV

Personalized SV

Point Of Interest

( )

image

@ailab.ics.keio.ac.jp

[email protected]

0.

1.

2.

λ:

Gi:

Gj:UP