# Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks

arXiv: https://arxiv.org/abs/2004.02182 · HTML: https://mlsanity.com/paper/2004.02182/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks](https://mlsanity.com/paper/2106.02817/) (2021)
- [Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models](https://mlsanity.com/paper/2111.14923/) (2021)
- [Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes](https://mlsanity.com/paper/2102.12894/) (2021)
- [A review of machine learning in processing remote sensing data for mineral exploration](https://mlsanity.com/paper/2103.07678/) (2021)
- [Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification](https://mlsanity.com/paper/2203.07815/) (2022)
- [Partial Resampling of Imbalanced Data](https://mlsanity.com/paper/2207.04631/) (2022)
- [A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs](https://mlsanity.com/paper/2310.13161/) (2023)
- [MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data](https://mlsanity.com/paper/2106.09643/) (2021)
- [Synthetic Embedding-based Data Generation Methods for Student Performance](https://mlsanity.com/paper/2101.00728/) (2021)

## Abstract (arXiv)

The fact that image datasets are often imbalanced poses an intense challenge for deep learning techniques. In this paper, we propose a method to restore the balance in imbalanced images, by coalescing two concurrent methods, generative adversarial networks (GANs) and capsule network. In our model, generative and discriminative networks play a novel competitive game, in which the generator generates samples towards specific classes from multivariate probabilities distribution. The discriminator of our model is designed in a way that while recognizing the real and fake samples, it is also requires to assign classes to the inputs. Since GAN approaches require fully observed data during training, when the training samples are imbalanced, the approaches might generate similar samples which leading to data overfitting. This problem is addressed by providing all the available information from both the class components jointly in the adversarial training. It improves learning from imbalanced data by incorporating the majority distribution structure in the generation of new minority samples. Furthermore, the generator is trained with feature matching loss function to improve the training convergence. In addition, prevents generation of outliers and does not affect majority class space. The evaluations show the effectiveness of our proposed methodology; in particular, the coalescing of capsule-GAN is effective at recognizing highly overlapping classes with much fewer parameters compared with the convolutional-GAN.