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Found 10 papers, 5 papers with code
CUTS: A Framework for Multigranular Unsupervised Medical Image Segmentation
To address this, we present CUTS (Contrastive and Unsupervised Training for multi-granular medical image Segmentation), a fully unsupervised deep learning framework for medical image segmentation to better utilize the vast majority of imaging data that are not labeled or annotated.
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points.
Improving Multi-Manifold GANs with a Learned Noise Prior
Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution.
Beyond GANs: Transforming without a Target Distribution
While generative neural networks can learn to transform a specific input dataset into a specific target dataset, they require having just such a paired set of input/output datasets.
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
The achievements of these models have been limited to a particular subset of domains where this assumption yields good results, namely homogeneous domains that are characterized by style or texture differences.
Finding Archetypal Spaces Using Neural Networks
Here, we propose a reformulation of the problem such that the goal is to learn a non-linear transformation of the data into a latent archetypal space.
Generating and Aligning from Data Geometries with Generative Adversarial Networks
Unsupervised domain mapping has attracted substantial attention in recent years due to the success of models based on the cycle-consistency assumption.
Interpretable Neuron Structuring with Graph Spectral Regularization
Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer.
MAGAN: Aligning Biological Manifolds
We present a new GAN called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that related points in each measurement space are aligned together.
Neural Attribute Machines for Program Generation
During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.
