To represent continuously changing environment, we assume a class-incremental model in lifelong learning, new classes emerge sequentially, and data distribution may shift within one class, mimicking a dynamic scenario like a self-driving vehicle.
The goal is to create a classification algorithm that maps X → Y. Evaluation involves constructing an iid dataset for periodic testing, maintaining balance among classes, even those unseen during training.
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