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I'm doing the same thing actually. I had to find tools because I was getting black images too. This one worked for me: import numpy as np import matplotlib.pyplot as plt from matplotlib.figure import Figure from matplotlib.backends.backend_agg import FigureCanvas from PIL import Image import tensorflow as tf keras_imager = tf.keras.preprocessing.image # ...


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Couple of Things straight up: Inject some noise in the process. When the gan or autoencoder learns that there is some noise it will start to generalise better Use weaker architectures. (Analogy to weak learners, you cant build random forest wit hstrong trees). Basically allowing for your Architecture to be weak enough to be able to generalise and not learn ...


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