  Class Incremental Learning (CIL) is inspired by the human ability to learn
new classes without forgetting previous ones. CIL becomes more challenging in
real-world scenarios when the samples in each incremental step are imbalanced.
This creates another branch of problem, called Generalized Class Incremental
Learning (GCIL) where each incremental step is structured more realistically.
Grow When Required (GWR) network, a type of Self-Organizing Map (SOM),
dynamically create and remove nodes and edges for adaptive learning. GWR
performs incremental learning from feature vectors extracted by a Convolutional
Neural Network (CNN), which acts as a feature extractor. The inherent ability
of GWR to form distinct clusters, each corresponding to a class in the feature
vector space, regardless of the order of samples or class imbalances, is well
suited to achieving GCIL. To enhance GWR's classification performance, a
high-quality feature extractor is required. However, when the convolutional
layers are adapted at each incremental step, the GWR nodes corresponding to
prior knowledge are subject to near-invalidation. This work introduces the Self
Augmenting Task Hallucinal Unified Representation (SATHUR), which
re-initializes the GWR network at each incremental step, aligning it with the
current feature extractor. Comprehensive experimental results demonstrate that
our proposed method significantly outperforms other state-of-the-art GCIL
methods on CIFAR-100 and CORe50 datasets.
