Bidirectional Conditional Generative Adversarial Networks
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Conditional variants of Generative Adversarial Networks (GANs), known as cGANs, are generative models that can produce data samples (x) conditioned on both latent variables (z) and known auxiliary information (c). Another GAN variant, Bidirectional GAN (BiGAN) is a recently developed framework for learning the inverse mapping from x to z through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN. We propose the Bidirectional Conditional GAN (BCGAN), which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from x to both z and c, trained simultaneously with the conditional generator and discriminator in an end-to-end setting. We present crucial techniques for training BCGANs, which incorporate an extrinsic factor loss along with an associated dynamically-tuned importance weight. As compared to other encoder-based GANs, BCGANs not only encode c more accurately but also utilize z and c more effectively and in a more disentangled way to generate data samples.
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