Advanced Deep Learning with Keras

Disentangled Representation GANs
2. Varying the first noise code, z 0
, as a constant vector from -4.0 to 4.0 for digits
0 to 9 as shown as follows. The second noise code, z 0
, is set to zero vector.
Figure 6.2.10 shows that the first noise code controls the thickness of the digit.
For example, for digit 8:
python3 stackedgan-mnist-6.2.1.py
--generator0=stackedgan_mnist-gen0.h5
--generator1=stackedgan_mnist-gen1.h5 --z0=0 --z1=0 –p0
--digit=8
3. Varying the second noise code, z 1
, as a constant vector from -1.0 to 1.0 for
digits 0 to 9 shown as follows. The first noise code, z 0
, is set to zero vector.
Figure 6.2.11 shows that the second noise code controls the rotation (tilt)
and to a certain extent the thickness of the digit. For example, for digit 8:
python3 stackedgan-mnist-6.2.1.py
--generator0=stackedgan_mnist-gen0.h5
--generator1=stackedgan_mnist-gen1.h5 --z0=0 --z1=0 –p1
--digit=8
Figure 6.2.9: Images generated by StackedGAN as the discrete code is varied from 0 to 9. Both z
0 and z
1 have
been sampled from a normal distribution with zero mean and 0.5 standard deviation.
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