Avoiding Catastrophic Forgetting in Visual Classification Using Human
  Concept Formation
Nicki Barari Xin Lian Christopher J. MacLellan
Deep neural networks have excelled in machine learning, particularly in
vision tasks, however, they often suffer from catastrophic forgetting when
learning new tasks sequentially. In this work, we propose Cobweb4V, a novel
visual classification approach that builds on Cobweb, a human like learning
system that is inspired by the way humans incrementally learn new concepts over
time. In this research, we conduct a comprehensive evaluation, showcasing the
proficiency of Cobweb4V in learning visual concepts, requiring less data to
achieve effective learning outcomes compared to traditional methods,
maintaining stable performance over time, and achieving commendable asymptotic
behavior, without catastrophic forgetting effects. These characteristics align
with learning strategies in human cognition, positioning Cobweb4V as a
promising alternative to neural network approaches.