PyTorch implementation of normalizing flow models
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Updated
Jul 9, 2024 - Python
PyTorch implementation of normalizing flow models
[NeurIPS 2022] (Amortized) distributional control for pre-trained generative models
Code for Transformed Distribution Matching (TDM) for Missing Value Imputation, ICML 2023
A python/pytorch package for invertible neural networks
Code for the paper "Guided Image Generation with Conditional Invertible Neural Networks" (2019)
FrEIA sample code
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
Official repository of "DeepMIH: Deep Invertible Network for Multiple Image Hiding", TPAMI 2022.
Constrained optimization toolkit for PyTorch
Official PyTorch implementation of "HiNet: Deep Image Hiding by Invertible Network" (ICCV 2021)
Learning inverse kinematics using invertible neural networks and GANs. Research project for "Advanced Deep Learning for Robotics".
Multi-fidelity Generative Deep Learning Turbulent Flows
MintNet: Building Invertible Neural Networks with Masked Convolutions
Results of my master thesis. Conditional invertible neural networks in the freia framework were used to dertermine the CO2 concentration using spectra taken by the satellite OCO2.
This contains my pytorch implementation of Glow from OpenAI.
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