Hey Shima Shafiee, here's an article which describes some methods in feature construction/ feature engineering including GANs.

https://towardsdatascience.com/automatic-feature-engineering-using-generative-adversarial-networks-8e24b3c16bf3

Thanks dear for sharing

It is so helpful,dear Christian Schmidt

Yes, Generative Adversarial Networks (GANs) can be used as a feature construct in various applications. GANs have been widely employed for feature learning and representation, where they can generate synthetic data that captures the underlying distribution of the training data. These synthetic data points can then be used as features or as a basis for feature extraction in downstream tasks.

One example of GAN-based feature construct is the use of GANs for unsupervised feature learning. In this approach, a GAN is trained to generate realistic synthetic samples that resemble the training data. The generator network in the GAN can be seen as learning a compressed representation of the data, capturing its important features. The latent space of the generator network can then be used as a feature representation for various tasks such as classification, clustering, or anomaly detection.

A notable reference for GAN-based feature construct is the paper "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks" by Alec Radford et al. This paper introduced a GAN architecture called DCGAN and demonstrated its effectiveness in learning meaningful representations from images without the need for explicit supervision. The learned representations were shown to exhibit useful properties such as disentangling factors of variation and enabling image morphing.

Reference: Alec Radford, Luke Metz, Soumith Chintala. "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks." arXiv preprint arXiv:1511.06434 (2015).

DEAR Muhammad Azam

thanks so much and your answer is so complete and helpful