Post on 01-Aug-2020
RBM
shoya@ailab.ics.keio.ac.jp kitagawa@ailab.ics.keio.ac.jp
● IL-RBM
●
akita@ailab.ics.keio.ac.jp
AR
sawada@ailab.ics.keio.ac.jp
Fig. Incremental Learning RBM
Fig. Fig.
okuoka@ailab.ics.keio.ac.jp
seno@ailab.ics.keio.ac.jp
●
●
SLAM
takimoto@ailab.ics.keio.ac.jp
●
●
SLAM
FastSLAM
●
● ( μ Σ
)
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javier.fernandez@ailab.ics.keio.ac.jp
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 …
enami@ailab.ics.keio.ac.jp
oto@ailab.ics.keio.ac.jp
→
U-net VAE
ishikawa@ailab.ics.keio.ac.jp
InfoGAN
abe@ailab.ics.keio.ac.jp
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
satogata@ailab.ics.keio.ac.jp
POI
yoshioka@ailab.ics.keio.ac.jp
1.
2. 3
fukuchi@ailab.ics.keio.ac.jp
→
Conventional SV
Personalized SV
Point Of Interest
( )
image
@ailab.ics.keio.ac.jp
↓
serizawa@ailab.ics.keio.ac.jp
0.
1.
2.
λ:
Gi:
Gj:UP