no code implementations • 17 Jun 2022 • Ari Heljakka, Martin Trapp, Juho Kannala, Arno Solin
This observed 'predictive' multiplicity (PM) also implies elusive differences in the internals of the models, their 'representational' multiplicity (RM).
2 code implementations • NeurIPS 2020 • Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin
These networks can faithfully reproduce individual real-world input images like regular autoencoders, but also generate a fused sample from an arbitrary combination of several such images, allowing instantaneous 'style-mixing' and other new applications.
1 code implementation • 6 Dec 2019 • Yuxin Hou, Ari Heljakka, Arno Solin
While frame-independent predictions with deep neural networks have become the prominent solutions to many computer vision tasks, the potential benefits of utilizing correlations between frames have received less attention.
1 code implementation • 12 Apr 2019 • Ari Heljakka, Arno Solin, Juho Kannala
retaining the identity of a face), sharp generated/reconstructed samples in high resolutions, and a well-structured latent space that supports semantic manipulation of the inputs.
1 code implementation • 9 Jul 2018 • Ari Heljakka, Arno Solin, Juho Kannala
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.
2 code implementations • 14 Feb 2018 • Ari Heljakka, Arno Solin, Juho Kannala
By treating the age phases as a sequence of image domains, we construct a chain of transformers that map images from one age domain to the next.