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Found 18 papers, 7 papers with code
Analyzing Inverse Problems with Invertible Neural Networks
Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters.
Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
Assessment of the specific hardware used in conjunction with such algorithms, however, has not properly addressed the possibility that the problem may be ill-posed.
Guided Image Generation with Conditional Invertible Neural Networks
We demonstrate these properties for the tasks of MNIST digit generation and image colorization.
Conditional Invertible Neural Networks for Guided Image Generation
In this work, we address the task of natural image generation guided by a conditioning input.
Out of distribution detection for intra-operative functional imaging
Multispectral optical imaging is becoming a key tool in the operating room.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification
In this work, firstly, we develop the theory and methodology of IB-INNs, a class of conditional normalizing flows where INNs are trained using the IB objective: Introducing a small amount of {\em controlled} information loss allows for an asymptotically exact formulation of the IB, while keeping the INN's generative capabilities intact.
BayesFlow: Learning complex stochastic models with invertible neural networks
In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.
Generative Classifiers as a Basis for Trustworthy Image Classification
Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities.
Learning Robust Models Using The Principle of Independent Causal Mechanisms
Standard supervised learning breaks down under data distribution shift.
Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging
Multispectral photoacoustic imaging (PAI) is an emerging imaging modality which enables the recovery of functional tissue parameters such as blood oxygenation.
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks
One example is the registration of 2D X-ray images with preoperative three-dimensional computed tomography (CT) images in intraoperative surgical guidance systems.
Benchmarking Invertible Architectures on Inverse Problems
Recent work demonstrated that flow-based invertible neural networks are promising tools for solving ambiguous inverse problems.
Conditional Invertible Neural Networks for Diverse Image-to-Image Translation
We introduce a new architecture called a conditional invertible neural network (cINN), and use it to address the task of diverse image-to-image translation for natural images.
Exoplanet Characterization using Conditional Invertible Neural Networks
Indeed, using cINNs allows for orders of magnitude faster inference of an exoplanet's composition than what is possible using an MCMC method, however, it still requires the computation of a large database of internal structures to train the cINN.
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare
Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability.
Towards Multimodal Depth Estimation from Light Fields
We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel.
Training Invertible Neural Networks as Autoencoders
Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks.
Application-driven Validation of Posteriors in Inverse Problems
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist.
