This paper explores incremental learning in machine learning, aiming to enhance system knowledge over time, crucial for IoT and social media applications. The authors review and implement state-of-the-art methodologies, including iCaRL, and propose new variations for specific use cases. They evaluate catastrophic forgetting, establish benchmarks, and seek to improve performance in continuous learning scenarios.
