{'fraction_non_alphanumeric': 0.011217948717948718, 'fraction_numerical': 0.0, 'mean_word_length': 5.578947368421052, '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}}
Adversarial Domain Adaptation via Class Correlation

Adversarial learning has been embedded in deep networks as a transferable representation of domain adaptation. The existing adversarial domain adaptation methods may not pay attention to the correlation between categories. In this paper, we propose an adversarial domain adaptation based on class correlation. In addition to the commonly used adversarial loss, we added a loss based on class correlation to reduce the probability of misclassification. We prevent the diversity of the network from decreasing by restricting the eigenvalues of the class correlation matrix.