What is the difference between the idea of transfer learning and applying foundation models?
As much as I understand, both methods use 'knowledge' gained from training on big amount of data to solve an unseen task. For example, a model can learn to understand English text and then be adjusted to write summaries.
CodePudding user response：
Transfer learning and applying foundation models are similar in that they both involve using knowledge gained from training a model on a large dataset to solve a new, related task. However, there are some key differences between the two concepts.
Transfer learning involves taking a pre-trained model that has already been trained on a large dataset and using it as a starting point to train a new model on a different, but related, dataset. For example, a model that has been trained to recognize objects in images could be used as the starting point to train a new model to classify medical images. By starting with a pre-trained model, transfer learning can save time and resources because the new model doesn't have to be trained from scratch.
On the other hand, applying foundation models involves using a pre-existing model as a component of a larger system to solve a new task. In this case, the pre-existing model is not necessarily trained on a large dataset, and it is not necessarily the starting point for training a new model. Instead, the pre-existing model is used as a building block to construct a more complex system that can solve the new task. For example, a foundation model that has been trained to recognize speech could be used as part of a larger system to transcribe audio recordings.
In summary, transfer learning involves using a pre-trained model as the starting point to train a new model on a different dataset, while applying foundation models involves using a pre-existing model as a component of a larger system to solve a new task. Both approaches can help save time and resources by leveraging existing knowledge, but they are used in slightly different ways.
CodePudding user response：
Applying foundation models* is just an example of transfer learning.
Transfer learning refers to machine learning methods that "transfer" knowledge from a source domain to a target domain. Here, domain can be interpreted in many ways: genre, language, task, etc. So transfer learning is very broad as it doesn't specify e.g., the form of the source domain knowledge, whether both the source and the target domain are accessible at training time, etc. Also, transfer learning has been studied long before the era of foundation models. Applying a foundation model is only one instance of transfer learning where
- the source domain knowledge is represented in the form of a pretrained model;
- domain is interpreted as task, and;
- if fine-tuning on the target domain is performed: the source domain data may not be accessible anymore, and the target domain has labeled data.
The list may be incomplete because there are many aspects based on which we can categorize transfer learning. Some examples of transfer learning that doesn't use foundation models include multi-task learning, cross-lingual learning via e.g., cross-lingual embedding, domain-adversarial training, and so on. I recommend reading Chapter 3 of the thesis by Sebastian Ruder for an overview of transfer learning in NLP.
*) There are controversies surrounding the term foundation model in NLP. At the moment, it is almost exclusively used by Stanford researchers; others in the NLP community don't use it that much. While most people would be familiar with the term, I suggest using pretrained model for now.