DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data
This addresses the challenge of imbalanced data for deep learning practitioners, offering a simpler alternative to GAN-based methods, though it appears incremental as it builds on existing SMOTE techniques.
The paper tackles the imbalanced data problem in deep learning by proposing DeepSMOTE, a novel oversampling method that fuses an encoder/decoder framework with SMOTE and a dedicated loss function, generating high-quality artificial images to balance training sets without requiring a discriminator.
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) SMOTE-based 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