{'fraction_non_alphanumeric': 0.019077901430842606, 'fraction_numerical': 0.0, 'mean_word_length': 5.0, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Loss Function of GAN to Make a Clear Judgment

In general, in GAN training, the Discriminator learns a loss function whose value becomes smaller when the expected label matches the correct label. However, if we do not make a clear decision about the authenticity of the image, the GAN will continue to learn about ambiguous images, and the resulting images will be less clear. In this experiment, we propose a loss function that penalizes the Discriminator when it makes an ambiguous decision by adding a regularization term to the existing loss function Binary Cross Entropy(BCE), and verify its superiority through experiments.