Latent Bernoulli Autoencoder

Jul 12, 2020



In this work, we pose a question whether it is possible to design and train an autoencoder model in an end-to-end fashion to learn latent representations in multivariate Bernoulli space, and achieve performance comparable with the current state-of-the-art variational methods. Moreover, we investigate how to generate novel samples and perform smooth interpolation in the binary latent space. To meet our objective, we propose a simplified deterministic model with a straight-through estimator to learn the binary latents and show its competitiveness with the latest VAE methods. Furthermore, we propose a novel method based on a random hyperplane rounding for sampling and smooth interpolation in the multivariate Bernoulli latent space. Although not a main objective, we demonstrate that our methods perform on par or better than the current state-of-the-art methods on common CelebA, CIFAR-10 and MNIST datasets. PyTorch code and trained models to reproduce published results will be released with the camera ready version.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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