  Class-Incremental Learning is a challenging problem in machine learning that
aims to extend previously trained neural networks with new classes. This is
especially useful if the system is able to classify new objects despite the
original training data being unavailable. While the semantic segmentation
problem has received less attention than classification, it poses distinct
problems and challenges since previous and future target classes can be
unlabeled in the images of a single increment. In this case, the background,
past and future classes are correlated and there exist a background-shift. In
this paper, we address the problem of how to model unlabeled classes while
avoiding spurious feature clustering of future uncorrelated classes. We propose
to use Evidential Deep Learning to model the evidence of the classes as a
Dirichlet distribution. Our method factorizes the problem into a separate
foreground class probability, calculated by the expected value of the Dirichlet
distribution, and an unknown class (background) probability corresponding to
the uncertainty of the estimate. In our novel formulation, the background
probability is implicitly modeled, avoiding the feature space clustering that
comes from forcing the model to output a high background score for pixels that
are not labeled as objects. Experiments on the incremental Pascal VOC, and
ADE20k benchmarks show that our method is superior to state-of-the-art,
especially when repeatedly learning new classes with increasing number of
increments.
