44. X
A
B
Y
N = 3
SelecEve ANenEon Model
(gX , gY )
centre of
the grid
centre of
the filter1,2
(µ1
X , µ2
Y )
µi
X = gX + (i N/2 0.5)
µj
Y = gY + (i N/2 0.5)
µ1
X = gX + (1 1.5 0.5)
= gX
µ2
Y = gY + (2 1.5 0.5)
= gY
N × N grid of Gaussian filters
distance
between
the filters
44
45. SelecEve ANenEon Model
(gX , gY )
X
A
B
Y
(µ1
X , µ2
Y )
N = 3
(˜gX , ˜gY , log 2
, log˜, log ) = W(hdec
)
gX =
A + 1
2
(˜gX + 1)
gY =
B + 1
2
(˜gY + 1)
=
max(A, B) 1
N 1
˜
parameters are determined by hdec
variance of the
Gaussian filters
45
46. SelecEve ANenEon Model
FY [i, b] =
1
ZY
exp(
(b µi
Y )2
2 2
)
horizontal and verEcal filter bank matrices
FX [i, a] =
1
ZX
exp(
(a µi
X )2
2 2
)
A
B
FX
FX[1, :]
FX[2, :]
FX[3, :]
horizontal filter bank matrix
verEcal filter bank matrix
FT
Y
FY [1, :]T
FY [2, :]T
FY [3, :]T
46
47. SelecEve ANenEon Model
read(x, ˆxt, hdec
t 1) = [FY xFT
X , FY ˆxtFT
X ]
N
N
intensity
horizontal
filter bank
matrix
verEcal
filter bank
matrix
FY FT
Xx
N
A
B
FY xFT
X
47
48. SelecEve ANenEon Model
(˜g0
X , ˜g0
Y , log 02
, log˜0
, log 0
, wt) = W(hdec
)
write(hdec
t ) =
1
ˆ
F0T
Y wtF0
X
F0T
Y F0
X
A
B
wt
A
B
F0T
Y wtF0
X
48
51. Training DRAW
L = Lx
+ Lz
total loss
= reconstrucEon loss + latent loss
w w ⌘
@L
@w
gradient descent
51
reconstrucEon and
x should be as
similar as possible.
z should be as
simple as possible.
52. GeneraEng New Images
˜zt ⇠ P(Zt)
˜hdec
t = RNNdec
(˜hdec
t 1, ˜zt)
˜ct = ˜ct 1 + write(˜hdec
t )
˜x ⇠ D(X|˜cT )
RNNdec RNNdec
canvas
D
˜zt
˜hdec
t
˜ct
˜cT
˜ct 1
˜hdec
t 1
52