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> To return to the point about image augmentations being hard to add: It's so easy to explain what your training code should do "Just distort the hue a bit" and there seem to be operations explicitly for that: https://www.tensorflow.org/api_docs/python/tf/image/adjust_h.... but when you go to train with them, you'll discover that backpropagation isn't implemented, i.e. they break in training code.

Why not do the data augmentation during preprocessing (so that the transformations don't have to be done by differentiable transforms)? I.e., map over a tf.Dataset with the transformation (and append to the original dataset).

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