Continual Learning Benchmark

Continual learning benchmarks evaluate machine learning models' ability to acquire new knowledge incrementally without forgetting previously learned information, a crucial aspect for real-world applications. Current research focuses on developing and testing novel algorithms and architectures, such as those employing low-rank adaptations, prompt engineering, and memory-based approaches, to mitigate "catastrophic forgetting" across diverse tasks and modalities (vision, language, audio). These benchmarks are vital for advancing the field by providing standardized evaluation protocols and driving the development of more robust and efficient continual learning systems, with implications for areas like personalized AI and autonomous systems.