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Found 6 papers, 5 papers with code
Disentangling Model Multiplicity in Deep Learning
This observed 'predictive' multiplicity (PM) also implies elusive differences in the internals of the models, their 'representational' multiplicity (RM).
Deep Automodulators
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.
Gaussian Process Priors for View-Aware Inference
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.
Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders
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.
Pioneer Networks: Progressively Growing Generative Autoencoder
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.
Recursive Chaining of Reversible Image-to-image Translators For Face Aging
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.
