InfoVAEGAN: Learning Joint Interpretable Representations by Information Maximization and Maximum Likelihood
By Fei Ye, Adrian G. Bors. 2021
16 citations recorded by the source corpus.
Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold. In this paper, we propose a novel representation learning algorithm which combines the inference abilities of Variational Autoencoders (VAE) with the generalization capability of Generative Adversarial Networks (GAN). The proposed model, called InfoVAEGAN, consists of three networks: Encoder, Generator and Discriminator. InfoVAEGAN aims to jointly learn discrete and continuous interpretable representations in an unsupervised manner by using two different data-free log-likelihood functions onto the variables sampled from the generator’s distribution. We propose a two-stage algorithm for optimizing the inference network separately from the generator training. Moreover, we enforce the learning of interpretable representations through the maximization of the mutual information between the existing latent variables and those created through generative and inference processes.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computer Vision and Pattern Recognition
In Statistical and Nonlinear Physics
Citations in the corpus
- Task-Free Continual Learning via Online Discrepancy Distance Learning (2022, neutral): “Due to its robust generation and inference mechanisms, VAEs have been widely used in many applications [48, 49, 50, 51, 54, 61].”
- Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022, methodology): “VAEs have been widely used in image synthesis [60,62], semi-supervised learning [1,63] and for image-to-image translation [38].”
- Learning an Evolved Mixture Model for Task-Free Continual Learning (2022, background): “However, deep learning systems would usually perform well on individual tasks [2, 3] but suffer from dramatic performance loss when training on several different tasks sequentially [4, 5, 6].”