Mitigating the effects of Severe Imbalance in Multi-class Semantic Segmentation

Class imbalance is one of the main weaknesses in modern machine learning methods. In this area, datasets with an imbalance ratio greater than 1:100 are defined as severely imbalanced. These require specific precautions and techniques to deal with the issue. In this thesis, different approaches to tackle the problem of severely imbalanced datasets in semantic segmentation are explored. Solutions such as resampling, the One-vs-Rest approach, and loss change are implemented and compared discussing their benefits and drawbacks. Furthermore, the delicate evaluation process is explained in all its complexity giving specific weight to the obtained results.