PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks

• Published in: Journal of mathematical imaging and vision Vol. 64; no. 5; pp. 569 - 585
• Main Authors: Pham, Chi-Hieu, Ladjal, Saïd, Newson, Alasdair
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer Nature B.V; Springer Verlag
• Subjects: Applications of Mathematics; Computer Science; Computer Vision and Pattern Recognition; Image Processing and Computer Vision; Machine Learning; Mathematical Methods in Physics; Photovoltaic cells; Principal components analysis; Signal,Image and Speech Processing; Statistics; Autoencoders; Generative networks; Image generation; autoencoders; image generation
• ISSN: 0924-9907; 1573-7683
• DOI: 10.1007/s10851-022-01077-z

Autoencoders and generative models produce some of the most spectacular deep learning results to date. However, understanding and controlling the latent space of these models presents a considerable challenge. Drawing inspiration from principal component analysis and autoencoders, we propose the principal component analysis autoencoder (PCA-AE). This is a novel autoencoder whose latent space verifies two properties. Firstly, the dimensions are organised in decreasing importance with respect to the data at hand. Secondly, the components of the latent space are statistically independent. We achieve this by progressively increasing the latent space during training, and with a covariance loss applied to the latent codes. The resulting autoencoder produces a latent space which separates the intrinsic attributes of the data into different components of the latent space, in a completely unsupervised manner. We also describe an extension of our approach to the case of powerful, pre-trained GANs. We show results on both synthetic examples of shapes and on a state-of-the-art GAN. For example, we are able to separate the colour shade scale of hair, pose of faces and gender, without accessing any labels. We compare the PCA-AE with other state-of-the-art approaches, in particular with respect to the ability to disentangle attributes in the latent space. We hope that this approach will contribute to better understanding of the intrinsic latent spaces of powerful deep generative models.