Data Imbalance in Deep Learning
Class imbalanced data is a common issue for deep learning models. When one or more classes have significantly fewer samples, the performance of deep learning models can suffer as they tend to prioritize learning from the majority class, resulting in poor generalization for the minority class(es).
A lot of real-world data is imbalanced, which presents challenges to deep learning classification tasks. Figure 6.1 shows some common categories of imbalanced data problems in various deep learning applications:
Figure 6.1 – Some common categories of imbalanced data problems
We will cover the following topics in this chapter:
- A brief introduction to deep learning
- Data imbalance in deep learning
- Overview of deep learning techniques to handle data imbalance
- Multi-label classification
By the end of this chapter, we’ll have a foundational understanding of deep learning and neural networks...
