164 research outputs found
Hypernetwork functional image representation
Motivated by the human way of memorizing images we introduce their functional
representation, where an image is represented by a neural network. For this
purpose, we construct a hypernetwork which takes an image and returns weights
to the target network, which maps point from the plane (representing positions
of the pixel) into its corresponding color in the image. Since the obtained
representation is continuous, one can easily inspect the image at various
resolutions and perform on it arbitrary continuous operations. Moreover, by
inspecting interpolations we show that such representation has some properties
characteristic to generative models. To evaluate the proposed mechanism
experimentally, we apply it to image super-resolution problem. Despite using a
single model for various scaling factors, we obtained results comparable to
existing super-resolution methods
Hypernetwork approach to generating point clouds
In this work, we propose a novel method for generating 3D point clouds that
leverage properties of hyper networks. Contrary to the existing methods that
learn only the representation of a 3D object, our approach simultaneously finds
a representation of the object and its 3D surface. The main idea of our
HyperCloud method is to build a hyper network that returns weights of a
particular neural network (target network) trained to map points from a uniform
unit ball distribution into a 3D shape. As a consequence, a particular 3D shape
can be generated using point-by-point sampling from the assumed prior
distribution and transforming sampled points with the target network. Since the
hyper network is based on an auto-encoder architecture trained to reconstruct
realistic 3D shapes, the target network weights can be considered a
parametrization of the surface of a 3D shape, and not a standard representation
of point cloud usually returned by competitive approaches. The proposed
architecture allows finding mesh-based representation of 3D objects in a
generative manner while providing point clouds en pair in quality with the
state-of-the-art methods
General hypernetwork framework for creating 3D point clouds
In this work, we propose a novel method for generating 3D point clouds that leverages properties of hypernetworks. Contrary to the existing methods that learn only the representation of a 3D object, our approach simultaneously finds a representation of the object and its 3D surface. The main idea of our HyperCloud method is to build a hypernetwork that returns weights of a particular neural network (target network) trained to map points from prior distribution into a 3D shape. As a consequence, a particular 3D shape can be generated using point-by-point sampling from the prior distribution and transforming sampled points with the target network. Since the hypernetwork is based on an auto-encoder architecture trained to reconstruct realistic 3D shapes, the target network weights can be considered as a parametrization of the surface of a 3D shape, and not as a standard representation of point cloud usually returned by competitive approaches. We also show that relying on hypernetworks to build 3D point cloud representations offers an elegant and flexible framework, and to that point we further extend our method by incorporating flow-based models which results in a novel HyperFlow approach
Learning the Effect of Registration Hyperparameters with HyperMorph
We introduce HyperMorph, a framework that facilitates efficient
hyperparameter tuning in learning-based deformable image registration.
Classical registration algorithms perform an iterative pair-wise optimization
to compute a deformation field that aligns two images. Recent learning-based
approaches leverage large image datasets to learn a function that rapidly
estimates a deformation for a given image pair. In both strategies, the
accuracy of the resulting spatial correspondences is strongly influenced by the
choice of certain hyperparameter values. However, an effective hyperparameter
search consumes substantial time and human effort as it often involves training
multiple models for different fixed hyperparameter values and may lead to
suboptimal registration. We propose an amortized hyperparameter learning
strategy to alleviate this burden by learning the impact of hyperparameters on
deformation fields. We design a meta network, or hypernetwork, that predicts
the parameters of a registration network for input hyperparameters, thereby
comprising a single model that generates the optimal deformation field
corresponding to given hyperparameter values. This strategy enables fast,
high-resolution hyperparameter search at test-time, reducing the inefficiency
of traditional approaches while increasing flexibility. We also demonstrate
additional benefits of HyperMorph, including enhanced robustness to model
initialization and the ability to rapidly identify optimal hyperparameter
values specific to a dataset, image contrast, task, or even anatomical region,
all without the need to retrain models. We make our code publicly available at
http://hypermorph.voxelmorph.net.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) at https://www.melba-journal.or
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