An Adaptive Random Path Selection Approach for Incremental Learning

Understand

In a conventional supervised learning setting, a machine learning model has access to examples of all object classes that are desired to be recognized during the inference stage.

• This results in a fixed model that lacks the flexibility to adapt to new learning tasks.
• In practical settings, learning tasks often arrive in a sequence and the models must continually learn to increment their previously acquired knowledge.
• Existing incremental learning approaches fall well below the state-of-the-art cumulative models that use all training classes at once.