8 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
Disentangling Model Multiplicity in Deep Learning
Ari Heljakka, Martin Trapp, Juho Kannala +1
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 simila…
Deep Automodulators
Ari Heljakka, Yuxin Hou, Juho Kannala +1
We introduce a new category of generative autoencoders called automodulators. These networks can faithfully reproduce individual real-world input images like regular autoencoders,…
Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders
Ari Heljakka, Arno Solin, Juho Kannala
We present a generative autoencoder that provides fast encoding, faithful reconstructions (eg. retaining the identity of a face), sharp generated/reconstructed samples in high reso…
Pioneer Networks: Progressively Growing Generative Autoencoder
Ari Heljakka, Arno Solin, Juho Kannala
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 th…