{'fraction_non_alphanumeric': 0.017890772128060263, 'fraction_numerical': 0.003766478342749529, 'mean_word_length': 4.84065934065934, '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}}
Evaluating GANs using Efﬁcient Real-World Estimate (Project Report - CSC2515)

Evaluation of generative models is a crucial task, and many metrics today ignore the reverse KL divergence component due to an existing difﬁculty in obtaining the real-world density estimate p r from the limited number of samples available. In this work, we propose a MCMC sampling method using Metropolis-Hastings algorithm for obtaining samples from p r as learned by the discriminator D of a GAN, using samples from the generator G . We improve upon an existing rejection sampling approach with respect to a higher acceptance percentage and less samples rejected around the modes. We show how this can be used to evaluate a metric containing a reverse KLD component, rather than adhering to a log-likelihood metric, which seems unfair for models like GANs which intend to minimize the Jenson-Shannon divergence in their loss function. This method can also be used for monitoring how well the generator has been trained with respect to the real-world estimate of the discriminator.