# Autoinverse: Uncertainty Aware Inversion of Neural Networks

arXiv: https://arxiv.org/abs/2208.13780 · HTML: https://mlsanity.com/paper/2208.13780/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 17 of 17)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks](https://mlsanity.com/paper/2203.09168/) (2022)
- [Deep neural networks for the evaluation and design of photonic devices](https://mlsanity.com/paper/2007.00084/) (2020)
- [Benchmarking deep inverse models over time, and the neural-adjoint method](https://mlsanity.com/paper/2009.12919/) (2020)
- [Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision](https://mlsanity.com/paper/1705.11166/) (2017)
- [Benchmarking Invertible Architectures on Inverse Problems](https://mlsanity.com/paper/2101.10763/) (2021)
- [Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures](https://mlsanity.com/paper/1902.03865/) (2019)
- [Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate](https://mlsanity.com/paper/2106.09019/) (2021)
- [Mixed Integer Neural Inverse Design](https://mlsanity.com/paper/2109.12888/) (2021)
- [Computational Design of Cold Bent Glass Faccades](https://mlsanity.com/paper/2009.03667/) (2020)

## Built on by (most cited first, 16 of 16)

- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [Compositional Generative Inverse Design](https://mlsanity.com/paper/2401.13171/) (2024)
- [Network Inversion of Convolutional Neural Nets](https://mlsanity.com/paper/2407.18002/) (2024)
- [Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics](https://mlsanity.com/paper/2308.05739/) (2023)
- [Shortcut Learning Susceptibility in Vision Classifiers](https://mlsanity.com/paper/2502.09150/) (2025)
- [3DID: Direct 3D Inverse Design for Aerodynamics with Physics-Aware Optimization](https://mlsanity.com/paper/2512.08987/) (2025)
- [Network Inversion for Generating Confidently Classified Counterfeits](https://mlsanity.com/paper/2503.20187/) (2025)
- [Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization](https://mlsanity.com/paper/2306.01095/) (2023)
- [Network Inversion for Training-Like Data Reconstruction](https://mlsanity.com/paper/2410.16884/) (2024)
- [Network Inversion for Uncertainty-Aware Out-of-Distribution Detection](https://mlsanity.com/paper/2505.23448/) (2025)
- [TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics](https://mlsanity.com/paper/2402.16930/) (2024)
- [Network Inversion and Its Applications](https://mlsanity.com/paper/2411.17777/) (2024)
- [Uncertainty separation via ensemble quantile regression](https://mlsanity.com/paper/2412.13738/) (2024)
- [TIE: A Training-Inversion-Exclusion Framework for Visually Interpretable and Uncertainty-Guided Out-of-Distribution Detection](https://mlsanity.com/paper/2512.00229/) (2025)
- [PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction](https://mlsanity.com/paper/2607.29378/) (2026)
- [Scientific Discovery under Validation Congestion via Multi-Fidelity Pairwise Rankings](https://mlsanity.com/paper/2610.01827/) (2026)

## Abstract (arXiv)

Neural networks are powerful surrogates for numerous forward processes. The inversion of such surrogates is extremely valuable in science and engineering. The most important property of a successful neural inverse method is the performance of its solutions when deployed in the real world, i.e., on the native forward process (and not only the learned surrogate). We propose Autoinverse, a highly automated approach for inverting neural network surrogates. Our main insight is to seek inverse solutions in the vicinity of reliable data which have been sampled form the forward process and used for training the surrogate model. Autoinverse finds such solutions by taking into account the predictive uncertainty of the surrogate and minimizing it during the inversion. Apart from high accuracy, Autoinverse enforces the feasibility of solutions, comes with embedded regularization, and is initialization free. We verify our proposed method through addressing a set of real-world problems in control, fabrication, and design.