NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation

Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1089-1099, 2021.

Abstract

In this paper, we present a non-parametric structured latent variable model for image generation, called NP-DRAW, which sequentially draws on a latent canvas in a part-by-part fashion and then decodes the image from the canvas. Our key contributions are as follows. 1) We propose a non-parametric prior distribution over the appearance of image parts so that the latent variable “what-to-draw” per step becomes a categorical random variable. This improves the expressiveness and greatly eases the learning compared to Gaussians used in the literature. 2) We model the sequential dependency structure of parts via a Transformer, which is more powerful and easier to train compared to RNNs used in the literature. 3) We propose an effective heuristic parsing algorithm to pre-train the prior. Experiments on MNIST, Omniglot, CIFAR-10, and CelebA show that our method significantly outperforms previous structured image models like DRAW and AIR and is competitive to other generic generative models. Moreover, we show that our model’s inherent compositionality and interpretability bring significant benefits in the low-data learning regime and latent space editing.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-zeng21b, title = {NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation}, author = {Zeng, Xiaohui and Urtasun, Raquel and Zemel, Richard and Fidler, Sanja and Liao, Renjie}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {1089--1099}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/zeng21b/zeng21b.pdf}, url = {https://proceedings.mlr.press/v161/zeng21b.html}, abstract = {In this paper, we present a non-parametric structured latent variable model for image generation, called NP-DRAW, which sequentially draws on a latent canvas in a part-by-part fashion and then decodes the image from the canvas. Our key contributions are as follows. 1) We propose a non-parametric prior distribution over the appearance of image parts so that the latent variable “what-to-draw” per step becomes a categorical random variable. This improves the expressiveness and greatly eases the learning compared to Gaussians used in the literature. 2) We model the sequential dependency structure of parts via a Transformer, which is more powerful and easier to train compared to RNNs used in the literature. 3) We propose an effective heuristic parsing algorithm to pre-train the prior. Experiments on MNIST, Omniglot, CIFAR-10, and CelebA show that our method significantly outperforms previous structured image models like DRAW and AIR and is competitive to other generic generative models. Moreover, we show that our model’s inherent compositionality and interpretability bring significant benefits in the low-data learning regime and latent space editing.} }
Endnote
%0 Conference Paper %T NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation %A Xiaohui Zeng %A Raquel Urtasun %A Richard Zemel %A Sanja Fidler %A Renjie Liao %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-zeng21b %I PMLR %P 1089--1099 %U https://proceedings.mlr.press/v161/zeng21b.html %V 161 %X In this paper, we present a non-parametric structured latent variable model for image generation, called NP-DRAW, which sequentially draws on a latent canvas in a part-by-part fashion and then decodes the image from the canvas. Our key contributions are as follows. 1) We propose a non-parametric prior distribution over the appearance of image parts so that the latent variable “what-to-draw” per step becomes a categorical random variable. This improves the expressiveness and greatly eases the learning compared to Gaussians used in the literature. 2) We model the sequential dependency structure of parts via a Transformer, which is more powerful and easier to train compared to RNNs used in the literature. 3) We propose an effective heuristic parsing algorithm to pre-train the prior. Experiments on MNIST, Omniglot, CIFAR-10, and CelebA show that our method significantly outperforms previous structured image models like DRAW and AIR and is competitive to other generic generative models. Moreover, we show that our model’s inherent compositionality and interpretability bring significant benefits in the low-data learning regime and latent space editing.
APA
Zeng, X., Urtasun, R., Zemel, R., Fidler, S. & Liao, R.. (2021). NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:1089-1099 Available from https://proceedings.mlr.press/v161/zeng21b.html.

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