# Conditional Idempotent Generative Networks

arXiv: https://arxiv.org/abs/2406.02841 · HTML: https://mlsanity.com/paper/2406.02841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 5 Jun 2024.

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

- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [GANSynth: Adversarial Neural Audio Synthesis](https://mlsanity.com/paper/1902.08710/) (2019)
- [ImagenHub: Standardizing the evaluation of conditional image generation models](https://mlsanity.com/paper/2310.01596/) (2023)
- [Evaluation Metrics for Conditional Image Generation](https://mlsanity.com/paper/2004.12361/) (2020)
- [Idempotent Generative Network](https://mlsanity.com/paper/2311.01462/) (2023)

## Abstract (arXiv)

We propose Conditional Idempotent Generative Networks (CIGN), a novel approach that expands upon Idempotent Generative Networks (IGN) to enable conditional generation. While IGNs offer efficient single-pass generation, they lack the ability to control the content of the generated data. CIGNs address this limitation by incorporating conditioning mechanisms, allowing users to steer the generation process towards specific types of data. We establish the theoretical foundations for CIGNs, outlining their scope, loss function design, and evaluation metrics. We then present two potential architectures for implementing CIGNs: channel conditioning and filter conditioning. Finally, we discuss experimental results on the MNIST dataset, demonstrating the effectiveness of both approaches. Our findings pave the way for further exploration of CIGNs on larger datasets and with more powerful computing resources to determine the optimal implementation strategy.