Understanding Knowledge Transferability for Transfer Learning: A Survey

Transfer learning's success depends on measuring knowledge transferability: The paper emphasizes that while transfer learning (reusing knowledge from one task to improve another) has become widespread in AI applications like image recognition and language processing, we still lack reliable methods to predict whether knowledge will actually transfer successfully—a gap that could lead to poor performance if the wrong source knowledge is selected.

Different metrics work better in different situations: The survey identifies that transferability metrics fall into different categories based on what type of knowledge they measure and how broadly they assess it. The paper stresses that "careful selection of these metrics" is necessary, meaning practitioners cannot use a one-size-fits-all approach and must match the metric to their specific application to avoid unreliable predictions.

Understanding why metrics work matters for trustworthy AI: The authors argue that understanding "the theoretical underpinnings of each transferability metric is critical" for building trustworthy AI systems. This means that for responsible AI deployment, organizations should not just use transferability metrics as black boxes but should understand the assumptions and limitations behind them.

Better transferability assessment could improve AI reliability and efficiency: By helping researchers and practitioners select appropriate metrics for their specific contexts, the paper suggests that more accurate transferability measurement could lead to "more efficient, reliable, and trustworthy AI systems"—reducing wasted computational resources and preventing failures from inappropriate knowledge transfer.