From Imbalance to Harmony: Empowering Models with Synthetic Data
Who doesn’t care about larger and more balanced training data? While over and under-sampling are established methods to deal with the imbalance issue, Ye-Bin et al. propose an alternative way to overcome imbalances through generative models in a fresh arxiv paper (https://arxiv.org/pdf/2308.00994.pdf). In the realm of visual tasks, deep neural networks (DNNs) have exhibited impressive […]