DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data

Abstract

Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have magnified the importance of the imbalanced data problem. The two main approaches to address this issue are based on loss function modifications and instance resampling. Instance sampling is typically based on Generative Adversarial Networks (GANs), which may suffer from mode collapse. Therefore, there is a need for an oversampling method that is specifically tailored to deep learning models, can work on raw images while preserving their properties, and is capable of generating high quality, artificial images that can enhance minority classes and balance the training set. We propose DeepSMOTE -a novel oversampling algorithm for deep learning models. It is simple, yet effective in its design. It consists of three major components: (i) an encoder/decoder framework; (ii) SMOTEbased oversampling; and (iii) a dedicated loss function that is enhanced with a penalty term. An important advantage of DeepSMOTE over GAN-based oversampling is that DeepSMOTE does not require a discriminator, and it generates high-quality artificial images that are both information-rich and suitable for visual inspection. DeepSMOTE code is publicly available at: https://github.com/dd1github/DeepSMOTE.

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

“…In a second experiment, we considered a more innovative generation approach called Deep Synthetic Minority Over-sampling Technique (DeepSMOTE) [33] (ID 7). Based on the traditional SMOTE technique [34] embedded in a deep encoder and decoder architecture, DeepSMOTE generates feature-rich images to diminish disadvantages of unbalanced classes.…”