The rise of deep learning methods in the areas like computer vision and natural language processing lead to the need of having massive datasets to train the deep neural networks. In most of the cases, finding large enough datasets is not possible and training a deep neural network from the scratch for a particular task may time consuming. For addressing this problem, transfer learning can be used as a learning framework; where the knowledge acquired from a learned related task is transferred for the learning improvement of a new task.
In a simple form, transfer learning helps a machine learning models to learn easily by getting the help from a pre-trained machine learning model which the domain is similar to some extent (not exactly similar).
There might be cases where transfer method actually decreases the performance, where we called them as a Negative Transfer. Normally, we (a human) engage with the task of deciding which knowledge can be transferred (mapping) in particular tasks but the active research is going on finding ways to do this mapping automatically.
That’s all about the theories! Let’s discuss how we can apply transfer learning in a computer vision task. As you all know, Convolutional Neural Networks (CNNs) is performing really well in the cases of image classification, image recognition and such tasks. Training deep CNNs need large amounts of image/video data and the massive number of parameter tuning operations takes a long time to train models. In such cases, Transfer Learning is a best fit to train new models and it is widely used in the industry as well as in the research.
There are three main approaches of using transfer learning in machine learning problems. To make it easier to understand I’ll get my examples from the context of training deep neural network models for computer vision (image classification, labeling etc.) related tasks.
ConvNet as fixed feature extractor –
In this case, you use a ConvNet that has been pre-trained with a large image repository like ImageNet and remove its last fully connected layer. The rest is used as a fixed feature extractor for the dataset you are having. Then a linear classifier (softmax or a linear SVM) should be trained for the new dataset.
Fine-tuning the ConvNet –
Here we are not just stopping by using the ConvNet as a feature extractor. We finetune the weights of the ConvNet with the data that we are having. Sometimes not the whole deepNet, the set of last layers are tuned as the first layers represent most generalized features.
Using pretrained models –
In here we used pre-trained models available in most deep learning frameworks and adjust them according to our need. In the next post, will discuss how to perform this using PyTorch.
One of the most important decisions to get in transfer learning is whether to fine tune the network or to leave it as it is. The size of the dataset and the similarity of the prevailing dataset to the model’s trained training set are the deciding factors for it. Here’s a summary that would help you to take the decision.
Let’s discuss how to perform transfer learning with an example in the next post. 😊