What Is AI Transfer Learning and How Does It Work?
If you’re interested in training your own AI model for natural language processing (NLP) or computer vision, you should familiarize yourself with transfer learning and how to use pre-trained models.
Without transfer learning, training an effective and reliable model will often be a resource-prohibitive endeavor, requiring lots of money, time, and expertise, with ChatGPT developer OpenAI estimated to have spent millions training GPT-3, GPT-3.5, and GPT-4. With the power of transfer learning, you can train your own model as powerful as the latest GPT model with little resources in a short period.
What Is AI Transfer Learning?
Transfer learning is the idea of taking a pre-trained model such as BERT or one of thedifferent GPT modelsand training it on a custom dataset to work on tasks it wasn’t necessarily trained to tackle.
For example, it’s possible to take a pre-trained model for classifying different cat species and train it to classify dogs. Through transfer learning, training your dog-classifying model should take significantly less time and resources to become as reliable as the original cat-classifying model.
This works since cats and dogs share many traits the pre-trained model can already identify. Since the cat-classifying model can identify the various traits of a cat, such as having four legs, fur coats, and prominent snouts, the dog-classifying model can skip all the training to identify those traits and inherit them from the original model. After inheriting all those neural networks, you then cut off the last layers of the trained model used to identify the more specific traits of a cat and replace them with a dataset specific to dogs.
What AI Models Can You Use for Transfer Learning?
To use transfer learning, you’ll need a pre-trained model. A pre-trained model is commonly known as an AI model trained for the purpose of gaining general knowledge on a particular subject or idea. These types of pre-trained models are purposely made for people to fine-tune and make more application-specific models. Some of the most popular pre-trained models are for NLP, likeBERT and GPT, and computer vision, such as VGG19 and Inceptionv3.
Although popular, these easily fine-tunable models aren’t the only ones you can use for transfer learning. You can also use models trained on tasks more specific than general object or language recognition. As long as the model has developed neural networks applicable to the model you’re trying to train, you can use just about any model for transfer learning.
You can get publicly available pre-trained models from places like TensorFlow Hub, Hugging Face, and the OpenAI model marketplace.
Benefits of Using AI Transfer Learning
Transfer learning provides several benefits over training an AI model from scratch.
Training an AI model from scratch is possible, but you need greater resources to do so.
How Does Transfer Learning Work?
In essence, there are three stages when it comes to transfer learning.
There is more to it than the three stages, but this outline details roughly how the AI transfer learning process works, with some fine-tuning.
Limitations to AI Transfer Learning
Although transfer learning is a valuable concept in training effective and reliable models, there are quite a few limitations that you need to know when using transfer learning to train a model.
So while transfer learning is a handy AI learning technique, limitations exist, and it isn’t a silver bullet.
Should You Use Transfer Learning?
Ever since the availability of pre-trained models, transfer learning has always been used to make more specialized models. There’s really no reason not to use transfer learning if there’s already a pre-trained model relevant to the problems your model will be solving.
Although it is possible to train a simple machine learning model from scratch, doing so on a deep learning model will require lots of data, time, and skill, which won’t make sense if you can repurpose an existing model similar to the one you plan to train. So, if you want to spend less time and money in training a model, try training your model through transfer learning.
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