Measuring Catastrophic Forgetting in Neural Networks

Understand

Deep neural networks are used in many state-of-the-art systems for machine perception.

• Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as flower recognition.
• When new tasks are added, typical deep neural networks are prone to catastrophically forgetting previous tasks.
• Networks that are capable of assimilating new information incrementally, much like how humans form new memories over time, will be more efficient than re-training the model from scratch each time a new task needs to be learned.