As humans, we have the ability to transfer our knowledge and skills learned in one context to new and unrelated tasks. I would like to call it as "transfer of learning" and I believe, it is a key aspect of human learning and development.
To make my case, consider a person who has learned to play the piano. This person has developed a range of skills and knowledge, such as the ability to read sheet music, understand musical notation, and coordinate their hand movements to play different notes and chords. These skills and knowledge are not specific to playing the piano and can be transferred to other tasks, such as playing a different musical instrument or composing music.
Allow me to provide another which I hope we all can relate and experienced, when we learn a new subject in school, we are often able to transfer that knowledge to other subjects or tasks. For example, if we learn about the scientific method in a biology class, we can apply that same method to a physics experiment or a social science study.
While it is core and true, transfer of learning is not always straightforward and can depend on a variety of factors, such as the similarity between the tasks, the amount of overlap in the knowledge and skills required, and the individual's prior knowledge and experiences. However, it is an important aspect of human learning and allows us to continually build upon our existing knowledge and skills to learn and adapt to new tasks and challenges.
So, let us explore together as we delve deeper into the topic of transfer learning for AI models, let us consider the benefits that can be gained from implementing this approach. What advantages can we expect to attain by utilizing transfer learning in our artificial intelligence models?
Transfer learning is a machine learning technique that has its roots in the field of psychology and the study of human learning. The idea behind transfer learning is that people are able to apply their knowledge and skills learned in one context to another.
By using pre-trained models that have already been trained on a large dataset, we can take advantage of the knowledge and features learned by the model and apply them to a new task. This allows us to significantly reduce the amount of labeled data and computation required to train a model on a new task, making it a powerful tool in the field of deep learning.
Transfer learning has become increasingly popular in recent years with the advancement of deep learning and the availability of large pre-trained models. It has been used to achieve state-of-the-art results in a wide range of tasks, such as image classification, natural language processing, and speech recognition.
One of the most common ways to utilize transfer learning is through the use of pre-trained models. These are models that have already been trained on a large dataset and can be fine-tuned for a specific task. Fine-tuning a pre-trained model involves adjusting the model's parameters so that it can better perform on the new task. This can be done by adding a few additional layers to the model and training those layers on the new task, or by unfreezing some of the layers in the pre-trained model and training them on the new data as well.
One of the main reasons transfer learning is so important is that it allows us to leverage the vast amounts of labeled data and computation that have already been used to train large models. For example, if we want to train a model to classify images of rose and poppy, we could start by using a pre-trained model that has already been trained on a large dataset of images, such as ImageNet. This model would already have learned features such as edges, shapes, and textures that are useful for identifying objects in images. By fine-tuning this pre-trained model on a smaller dataset of rose and poppy images, we can significantly reduce the amount of labeled data and computation required to train a model that performs well on this task.
If that does not excite you yet, then consider another reason transfer learning is important is that it can help improve the performance of a model on a new task. For example, if we want to train a model to perform natural language processing (NLP) tasks such as language translation or text classification, we can use a pre-trained model that has already been trained on a large dataset of text data. This pre-trained model would already have learned useful features such as word embeddings, which can be fine-tuned on the new NLP task. By doing so, we can often achieve better performance on the new task compared to training a model from scratch.
we all like stories, I do...., so.... consider the following story:
Imagine you are a researcher working on a project to identify the species of animals in photographs. You have a dataset of 50,000 labeled images of various animal species, but it is not enough to train a deep learning model from scratch to achieve good performance on this task. However, you realize that you can use transfer learning to fine-tune a pre-trained model that has already been trained on a large dataset of images, such as ImageNet ( I love object detection models !!!).
You decide to use a pre-trained model that was trained on ImageNet and has achieved state-of-the-art performance on a wide range of image classification tasks. You fine-tune this model on your dataset of animal images, adding a few additional layers and training those layers on the new task. After just a few epochs (O.. btw, epoch refers to a complete pass through a dataset during training. In simple words one or more iterations through the dataset) of training, you are able to achieve an accuracy of over 95% on your animal classification task, which is a significant improvement over what you would have been able to achieve by training a model from scratch on your limited dataset.
This story illustrates the power of transfer learning in helping us achieve good performance on a new task with limited labeled data and computation.
There are several techniques that can be used in transfer learning for AI models, listed below which I know of so far. but there will be many, I may have over simplified it, but just trying put the way I understood.
Fine-tuning a pre-trained model: as mentioned before, Fine-tuning involves adjusting the parameters of a pre-trained model to better perform on a new task. Fine-tuning can be performed using a variety of optimization algorithms, one I know of its stochastic gradient descent (SGD). I am still a learner, but trying to put my understanding of how we can do fine tuning as below.
Let's say we have a pre-trained model f(x) that takes in an input x and outputs a prediction y.
We want to fine-tune this model for a new task using a dataset
D = {(x_1, y_1), (x_2, y_2), ..., (x_n, y_n)}.
We can do this by minimizing the loss L(y, f(x)) using SGD. The update equation for the weights w of the model can be written as:
w = w - alpha * gradient(L(y, f(x)))
Where alpha is the learning rate and gradient(L(y, f(x))) is the gradient of the loss with respect to the weights w.
By performing this update at each step of SGD, we can fine-tune the pre-trained model f(x) on the new task using the dataset D.
Using a pre-trained model as a feature extractor: In this technique, the pre-trained model is used to extract features from the new data, which are then fed into a separate model that is trained on the new task. This can be useful when the new data is very different from the data used to train the pre-trained model, as the pre-trained model may not be able to directly classify the new data.
Using a pre-trained model as a fixed feature extractor: In this technique, the pre-trained model is used to extract features from the new data, but the weights of the pre-trained model are not updated during training. This can be useful when the new data is very different from the data used to train the pre-trained model and the pre-trained model is not able to directly classify the new data.
So in my opinion, transfer learning is a revolutionary technique in the field of artificial intelligence that has the power to greatly improve the performance and efficiency of our AI models. I feel, Whether you are a beginner or an experienced AI practitioner, transfer learning should be an essential tool in your AI development toolkit, offering the potential to unlock the full potential of your models and drive innovation in the field of artificial intelligence. So don't wait any longer - start exploring the exciting world of transfer learning today and see the transformative power it can bring to your AI projects!
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