Navigating transfer learning

Transfer learning can enhance how marketing professionals leverage AI by enabling the more effective use of pre-trained models on new tasks with only minor adjustments. While FSL uses a set of examples from the new task for quick adaptation, transfer learning focuses on repurposing an existing model without needing additional examples from the new domain.

This approach capitalizes on the knowledge that models gain from large-scale data in previous tasks, applying it to enhance marketing efforts in completely different areas without the overhead of retraining the model from scratch. Put differently, FSL improves model adaptability using very limited data examples, whereas transfer learning excels in environments where the relationship between past and current tasks is strong but the availability of large enough labeled datasets for training a base model for the new task is difficult or costly to acquire.

An additional advantage of transfer learning...