Transfer learning is a technique that can be used to speed up the training of machine learning models and improve their accuracy.
The current state of AI technology requires that data be manually labeled in order for machines to learn from it. This process is both time-consuming and expensive. Additionally, it limits the types of data that can be used for training, as only data that can be easily labeled is suitable. This often results in biased models which do not generalize well.
One possible solution to this problem is to use transfer learning. Transfer learning is a technique whereby knowledge learned by one model is transferred to another model. This allows for the reuse of existing models and knowledge, which can speed up the training process and reduce the cost of label acquisition.
In order to transfer knowledge between models, there must be some similarity between the models. For example, if you are trying to transfer knowledge from a task-specific model to a general-purpose model, the models must share some task-specific knowledge. However, even if the models are not identical, it may still be possible to transfer knowledge if the models are similar in some other way (e.g. they use the same input representation or architecture).
Transfer learning has been shown to be effective for a variety of tasks, including image classification, object detection, and question answering. It is also a promising approach for addressing the issue of data scarcity, as it can allow models to learn from data that would otherwise be unavailable.
There are a few challenges that need to be addressed in order to make transfer learning more widely applicable. Firstly, it is often difficult to identify when transfer learning will be beneficial. Secondly, there is a lack of understanding of how to effectively transfer knowledge between models. Finally, many existing approaches make assumptions about the similarity between models that are not always valid.
Despite these challenges, transfer learning is a promising approach that could address some of the limitations of current AI technology. With further research, it has the potential to improve the efficiency of training and the accuracy of models.
References:
https://en.wikipedia.org/wiki/Transfer_learning
https://towardsdatascience.com/a-guide-to-transfer-learning-f80e49fdd1c0
https://machinelearningmastery.com/transfer-learning-for-deep-learning/