# Adversarially Approximated Autoencoder for Image Generation and Manipulation

arXiv: https://arxiv.org/abs/1902.05581 · HTML: https://mlsanity.com/paper/1902.05581/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 12 of 12)

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- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
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- [Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study](https://mlsanity.com/paper/2104.15050/) (2021)
- [Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks](https://mlsanity.com/paper/2102.05582/) (2021)
- [Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare](https://mlsanity.com/paper/2103.10478/) (2021)
- [Towards Generating Realistic Underwater Images](https://mlsanity.com/paper/2505.14296/) (2025)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)

## Abstract (arXiv)

Regularized autoencoders learn the latent codes, a structure with the regularization under the distribution, which enables them the capability to infer the latent codes given observations and generate new samples given the codes. However, they are sometimes ambiguous as they tend to produce reconstructions that are not necessarily faithful reproduction of the inputs. The main reason is to enforce the learned latent code distribution to match a prior distribution while the true distribution remains unknown. To improve the reconstruction quality and learn the latent space a manifold structure, this work present a novel approach using the adversarially approximated autoencoder (AAAE) to investigate the latent codes with adversarial approximation. Instead of regularizing the latent codes by penalizing on the distance between the distributions of the model and the target, AAAE learns the autoencoder flexibly and approximates the latent space with a simpler generator. The ratio is estimated using generative adversarial network (GAN) to enforce the similarity of the distributions. Additionally, the image space is regularized with an additional adversarial regularizer. The proposed approach unifies two deep generative models for both latent space inference and diverse generation. The learning scheme is realized without regularization on the latent codes, which also encourages faithful reconstruction. Extensive validation experiments on four real-world datasets demonstrate the superior performance of AAAE. In comparison to the state-of-the-art approaches, AAAE generates samples with better quality and shares the properties of regularized autoencoder with a nice latent manifold structure.