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Found 10 papers, 4 papers with code
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 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.
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.
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation.
Learning to Push the Limits of Efficient FFT-Based Image Deconvolution
This work addresses the task of non-blind image deconvolution.
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.
Technical report: Training Mixture Density Networks with full covariance matrices
Mixture Density Networks are a tried and tested tool for modelling conditional probability distributions.
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 Guided Image Generation
In this work, we address the task of natural image generation guided by a conditioning input.
Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models
Simulating abundances of stable water isotopologues, i. e. molecules differing in their isotopic composition, within climate models allows for comparisons with proxy data and, thus, for testing hypotheses about past climate and validating climate models under varying climatic conditions.
