Model multiplicity is a well-known but poorly understood phenomenon that undermines the generalisation guarantees of machine learning models. It appears when two models with similar training-time performance differ in their predictions and real-world performance characteristics. This observed 'predictive' multiplicity (PM) also implies elusive differences in the internals of the models, their 'representational' multiplicity (RM). We introduce a conceptual and experimental setup for analysing RM by measuring activation similarity via singular vector canonical correlation analysis (SVCCA). We show that certain differences in training methods systematically result in larger RM than others and evaluate RM and PM over a finite sample as predictors for generalizability. We further correlate RM with PM measured by the variance in i.i.d. and out-of-distribution test predictions in four standard image data sets. Finally, instead of attempting to eliminate RM, we call for its systematic measurement and maximal exposure.

We build on recent advances in progressively growing generative autoencoder models. These models can encode and reconstruct existing images, and generate novel ones, at resolutions comparable to Generative Adversarial Networks (GANs), while consisting only of a single encoder and decoder network. The ability to reconstruct and arbitrarily modify existing samples such as images separates autoencoder models from GANs, but the output quality of image autoencoders has remained inferior. The recently proposed PIONEER autoencoder can reconstruct faces in the $256{\times}256$ CelebAHQ dataset, but like IntroVAE, another recent method, it often loses the identity of the person in the process. We propose an improved and simplified version of PIONEER and show significantly improved quality and preservation of the face identity in CelebAHQ, both visually and quantitatively. We also show evidence of state-of-the-art disentanglement of the latent space of the model, both quantitatively and via realistic image feature manipulations. On the LSUN Bedrooms dataset, our model also improves the results of the original PIONEER. Overall, our results indicate that the PIONEER networks provide a way to photorealistic face manipulation.

We introduce a novel generative autoencoder network model that learns to encode and reconstruct images with high quality and resolution, and supports smooth random sampling from the latent space of the encoder. Generative adversarial networks (GANs) are known for their ability to simulate random high-quality images, but they cannot reconstruct existing images. Previous works have attempted to extend GANs to support such inference but, so far, have not delivered satisfactory high-quality results. Instead, we propose the Progressively Growing Generative Autoencoder (PIONEER) network which achieves high-quality reconstruction with $128{\times}128$ images without requiring a GAN discriminator. We merge recent techniques for progressively building up the parts of the network with the recently introduced adversarial encoder-generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in CelebA inference tasks.