Dual Contradistinctive Generative Autoencoder

We present a new generative autoencoder model with dualcontradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for the reconstruction/synthesis), both being contradistinctive. Extensive experimental results by DC-VAE across different res-olutions including 32 x 32, 64 x 64, 128 x 128, and 512 x 512 are reported. The two contradistinctive losses in VAE work harmoniously in DC-VAE leading to a significant qualitative and quantitative performance enhancement over the baseline VAEs without architectural changes. State-of-the-art or competitive results among generative autoencoders for image reconstruction, image synthesis, image interpolation,and representation learning are observed. DC-VAE is ageneral-purpose VAE model, applicable to a wide variety of downstream tasks in computer vision and machine learning.