Is Continual Learning Ready for Real-world Challenges?

Understand

Despite continual learning's long and well-established academic history, its application in real-world scenarios remains rather limited.

• This paper contends that this gap is attributable to a misalignment between the actual challenges of continual learning and the evaluation protocols in use, rendering proposed solutions ineffective for addressing the complexities of real-world setups.
• We validate our hypothesis and assess progress to date, using a new 3D semantic segmentation benchmark, OCL-3DSS.
• We investigate various continual learning schemes from the literature by utilizing more realistic protocols that necessitate online and continual learning for dynamic, real-world scenarios (eg., in robotics and 3D vision applications).