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Found 8 papers, 2 papers with code
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
Learning to Deblur and Rotate Motion-Blurred Faces
Our method handles the complexity of face blur by implicitly learning the geometry and motion of faces through the joint training on three large datasets: FFHQ and 300VW, which are publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we built.
Neural network wave functions and the sign problem
Such states are the likely cause of the obstruction for NQS-based variational Monte Carlo to access the true ground states of these systems.
Unsupervised Generative 3D Shape Learning from Natural Images
In this paper we present, to the best of our knowledge, the first method to learn a generative model of 3D shapes from natural images in a fully unsupervised way.
Unsupervised 3D Shape Learning from Image Collections in the Wild
To achieve realism, the generative model is trained adversarially against a discriminator that tries to distinguish between the output of the renderer and real images from the given data set.
FaceShop: Deep Sketch-based Face Image Editing
We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest.
Disentangling Factors of Variation by Mixing Them
We learn our representation without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively; second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding degenerate feature mappings where some chunks are completely ignored.
Challenges in Disentangling Independent Factors of Variation
Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.
