# Network Inversion of Convolutional Neural Nets

arXiv: https://arxiv.org/abs/2407.18002 · HTML: https://mlsanity.com/paper/2407.18002/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Model Inversion Networks for Model-Based Optimization](https://mlsanity.com/paper/1912.13464/) (2019)
- [Autoinverse: Uncertainty Aware Inversion of Neural Networks](https://mlsanity.com/paper/2208.13780/) (2022)
- [Landscape Learning for Neural Network Inversion](https://mlsanity.com/paper/2206.09027/) (2022)
- [Network Inversion of Binarised Neural Nets](https://mlsanity.com/paper/2402.11995/) (2024)

## Built on by (most cited first, 8 of 8)

- [Shortcut Learning Susceptibility in Vision Classifiers](https://mlsanity.com/paper/2502.09150/) (2025)
- [Network Inversion for Generating Confidently Classified Counterfeits](https://mlsanity.com/paper/2503.20187/) (2025)
- [Network Inversion for Uncertainty-Aware Out-of-Distribution Detection](https://mlsanity.com/paper/2505.23448/) (2025)
- [Activation Matching for Explanation Generation](https://mlsanity.com/paper/2509.23051/) (2025)
- [EXP-CAM: Explanation Generation and Circuit Discovery Using Classifier Activation Matching](https://mlsanity.com/paper/2509.25686/) (2025)
- [TIE: A Training-Inversion-Exclusion Framework for Visually Interpretable and Uncertainty-Guided Out-of-Distribution Detection](https://mlsanity.com/paper/2512.00229/) (2025)
- [Med-CAM: Minimal Evidence for Explaining Medical Decision Making](https://mlsanity.com/paper/2604.13695/) (2026)
- [PTP: Previous-Token Prediction based LLM Inversion for Near-Exact Prompt Reconstruction](https://mlsanity.com/paper/2607.29378/) (2026)

## Abstract (arXiv)

Neural networks have emerged as powerful tools across various applications, yet their decision-making process often remains opaque, leading to them being perceived as "black boxes." This opacity raises concerns about their interpretability and reliability, especially in safety-critical scenarios. Network inversion techniques offer a solution by allowing us to peek inside these black boxes, revealing the features and patterns learned by the networks behind their decision-making processes and thereby provide valuable insights into how neural networks arrive at their conclusions, making them more interpretable and trustworthy. This paper presents a simple yet effective approach to network inversion using a meticulously conditioned generator that learns the data distribution in the input space of the trained neural network, enabling the reconstruction of inputs that would most likely lead to the desired outputs. To capture the diversity in the input space for a given output, instead of simply revealing the conditioning labels to the generator, we encode the conditioning label information into vectors and intermediate matrices and further minimize the cosine similarity between features of the generated images.