Few-shot learning and lifelong learning are connected through their focus on efficient adaptation and knowledge retention. Few-shot learning enables models to learn new tasks with minimal data, which is critical for lifelong learning systems that must continuously adapt without forgetting prior knowledge. By reducing reliance on large datasets, few-shot methods help lifelong learners integrate new information incrementally, avoiding the need to retrain from scratch. For instance, a lifelong learning system could use few-shot techniques to recognize a new object category in images after seeing only a handful of examples, while preserving its ability to classify older categories it learned earlier.
The relationship becomes clearer when considering how both paradigms address scalability. In lifelong learning, a model accumulates knowledge over time, but storing all historical data for retraining is impractical. Few-shot learning provides a way to learn from sparse data, making it feasible to add new tasks incrementally. For example, a robot designed for lifelong learning might use few-shot methods to quickly adapt to a new tool after minimal demonstrations, while retaining proficiency with tools it already knows. Techniques like meta-learning (e.g., Model-Agnostic Meta-Learning, or MAML) are often used here: the model is pre-trained to rapidly adapt to new tasks with limited data, aligning with lifelong learning’s goal of continuous adaptation. This synergy allows systems to stay compact and efficient as they evolve.
However, challenges remain in balancing stability (retaining old knowledge) and plasticity (learning new tasks). Few-shot methods alone don’t inherently prevent catastrophic forgetting—where a model overwrites old knowledge when learning something new. Developers often combine few-shot approaches with lifelong learning techniques like elastic weight consolidation (EWC), which identifies critical parameters for past tasks and restricts their adjustment during new training. For example, a multilingual chatbot using lifelong learning might employ few-shot learning to add support for a new language with limited examples, while EWC ensures existing language skills aren’t degraded. Future work could focus on better integrating these methods, such as using dynamic architectures that allocate dedicated subnetworks for new tasks learned via few-shot examples.