Learning is a crucial aspect of human life, enabling us to adapt to new environments and acquire new skills. Similarly, machine learning models need to learn continuously to improve their performance. Continual learning is the process of learning in an uncertain and changing environment, where models must adapt to new tasks while preserving old knowledge. In this article, we explore the challenges of continual learning and propose strategies for improving adaptability in machine learning models.
Adaptability
Adaptability is crucial for machine learning models to survive interruptions and memory loss during learning. The brain selectively transfers short-term memory into long-term memory, which is more robust, to avoid catastrophic forgetting. Similarly, a continual learning model needs to have strong adaptability to quickly transfer to new tasks while protecting old knowledge from interference by new information. We evaluate adaptability from two dimensions: knowledge transfer and knowledge retention.
Knowledge Transfer
Knowledge transfer refers to the capacity of a model to rapidly adapt to new tasks without forgetting old knowledge. A model with strong knowledge transfer can learn new tasks while preserving old knowledge, enabling it to continue learning without interruption. We measure knowledge transfer by analyzing the average accuracy across tasks combined with BWT (Bayesian Word Embeddings).
Knowledge Retention
Knowledge retention refers to the capacity of a model to preserve old knowledge and skills while learning new tasks. A model with strong knowledge retention can avoid catastrophic forgetting, ensuring that old knowledge is not lost during the learning process. We measure knowledge retention by analyzing the performance of a model in varying lengths of task sequence.
Evaluation Protocols
To evaluate adaptability, we use a combination of metrics from knowledge transfer and knowledge retention. We analyze the average accuracy across tasks combined with BWT to measure knowledge transfer, and examine the model’s performance in varying lengths of task sequence to assess knowledge retention.
Conclusion
In conclusion, continual learning is essential for machine learning models to learn and adapt continuously. A model’s adaptability is crucial to avoid interruption and memory loss during learning. We propose strategies for improving adaptability by measuring knowledge transfer and retention. By evaluating adaptability using these metrics, we can create more robust and efficient machine learning models that can survive in an uncertain and changing environment.
