Transfer learning involves applying knowledge from one task (often rather general with a large labeled dataset) to another task (often more specific with fewer labeled data). This is typically done by using the pre-trained model parameters from the general task as initialization for the finetuning of model parameters for the specific task. Meta-learning in contrast focuses on learning strategies for quickly adapting a model to multiple equally specific tasks.