Bridging The GAP: Simultaneous Fine Tuning for Data Re-Balancing
Bridging The GAP: Simultaneous Fine Tuning for Data Re-Balancing
There are many real-world classification problems wherein the issue of data imbalance (the case when a data set contains substantially more samples for one/many classes than the rest) is unavoidable. While under-sampling the problematic classes is a common solution, this is not a compelling option when the large data class …