Gallery Compare with Ground TruthĬursive Script (Requested by SNS audience)ĭownload tons of fonts as you please Requirement It is recommended that you enable this after d_loss flatlines around zero, for further tuning. You can enable label shuffling by setting flip_labels=1 option in train.py script. Empirically, label shuffling improves the model's generalization on unseen data with better details, and decrease the required number of characters.
The shuffled set likely will not have the corresponding target images to compute L1_Loss, but can be used as a good source for all other losses, forcing the model to further generalize beyond the limited set of provided examples. Specifically, within a given minibatch, for the same set of source characters, we generate two sets of target characters: one with correct embedding labels, the other with the shuffled labels. Label Shuffling mitigate this problem by presenting new challenges to the model. Updated Model with Label ShufflingĪfter sufficient training, d_loss will drop to near zero, and the model's performance plateaued.
The network structure is based off pix2pix with the addition of category embedding and two other losses, category loss and constant loss, from AC-GAN and DTN respectively. zi2zi(字到字, meaning from character to character) is an application and extension of the recent popular pix2pix model to Chinese characters.ĭetails could be found in this blog post. Learning eastern asian language typefaces with GAN. Zi2zi: Master Chinese Calligraphy with Conditional Adversarial Networks
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