# Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2111.04019 · HTML: https://mlsanity.com/paper/2111.04019/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Evolutionary Generative Adversarial Networks](https://mlsanity.com/paper/1803.00657/) (2018)
- [Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.04621/) (2019)
- [Annealing Genetic GAN for Minority Oversampling](https://mlsanity.com/paper/2008.01967/) (2020)
- [Does Adversarial Oversampling Help us?](https://mlsanity.com/paper/2108.10697/) (2021)

## Built on by (most cited first, 2 of 2)

- [Latent Preserving Generative Adversarial Network for Imbalance classification](https://mlsanity.com/paper/2209.01555/) (2022)
- [Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary Classifier](https://mlsanity.com/paper/2209.02881/) (2022)

## Abstract (arXiv)

This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification. It is an end-to-end approach in which different generative objective losses are considered in the generator network to improve the classification performance of the discriminator network. Thus, the same discriminator network has been used as a standard classifier by embedding the classifier network on top of the discriminating function. The effectiveness of the proposed method has been validated through two hyperspectral spatial-spectral data sets. The same generative and discriminator architectures have been utilized with two different GAN objectives for a fair performance comparison with the proposed method. It is observed from the experimental validations that the proposed method outperforms the state-of-the-art methods with better classification performance.