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Paper by Federico Baldassarre, Diego González Morín, Lucas Rodés-Guirao: arXiv:1712.03400 [cs.CV] Deep Koalarization
Some Predicted Results
Test Images
Generated Images
Network Architecture
Encoder Network Architecture
Layer
Filters
Kernel Size
Strides
Padding
Activation
Conv2D_E1
64
(3 × 3)
(2 × 2)
same
ReLU
Conv2D_E2
128
(3 × 3)
(1 × 1)
same
ReLU
Conv2D_E3
128
(3 × 3)
(2 × 2)
same
ReLU
Conv2D_E4
256
(3 × 3)
(1 × 1)
same
ReLU
Conv2D_E5
256
(3 × 3)
(2 × 2)
same
ReLU
Conv2D_E6
512
(3 × 3)
(1 × 1)
same
ReLU
Conv2D_E7
512
(3 × 3)
(1 × 1)
same
ReLU
Conv2D_E8
256
(3 × 3)
(1 × 1)
same
ReLU
Fusion Network Architecture
Layer
Filters
Kernel Size
Strides
Padding
Activation
Conv2D_F1
256
(1 × 1)
(1 × 1)
same
ReLU
Decoder Network Architecture
Layer
Filters
Kernel Size
Strides
Padding
Activation
Conv2D_D1
128
(3 × 3)
(1 × 1)
same
ReLU
UpSamp2D_D1
-
-
-
-
-
Conv2D_D2
64
(3 × 3)
(1 × 1)
same
ReLU
Conv2D_D3
64
(3 × 3)
(1 × 1)
same
ReLU
UpSamp2D_D2
-
-
-
-
-
Conv2D_D4
32
(3 × 3)
(1 × 1)
same
ReLU
Conv2D_D5
2
(3 × 3)
(1 × 1)
same
tanh
UpSamp2D_D2
-
-
-
-
-
Fusion Layer Architecture
High-Level Feature Extraction through Inception Resnet v2
The Inception Resnet v2 Model extracts the high-level features of the input grayscale image. The last layer before the softmax activation outputs a vector of size 1000 or dimension (1000 × 1 × 1) (feature-vector). This vector is repeated 28 × 28 times and then reshaped into a volume of (28 × 28 × 1000). This volume is then concatenated depth-wise to the Conv2D_E8 layer. This whole block of size (28 * 28 * 1256) is then passed through Conv2D_F1.