Achieving state-of-the-art performance in machine learning requires a lot of data and computational power. It takes a long time to learn to perform complex tasks like image recognition and natural language processing from scratch. Transfer learning is a way to shorten this process by leveraging pre-trained models. In this article, we’ll explore a few different ways to use transfer learning in classification. Pre-trained models are networks that have been trained on large datasets and achieved good accuracy in a particular domain.
They can be used as a starting point to build a model for another task. This can be a good strategy when you have limited resources or you don’t have the time to train your model from scratch. Using a pre-trained model can help reduce the number of layers in your model and reduce overfitting. This is important because overfitting occurs when a new model picks up too many irrelevant features from the training dataset. A pre-trained model can help avoid this problem by only focusing on the features it is meant to identify. For example, if you have a pre-trained model that is trained to recognize backpacks in images, you can use it to classify sunglasses. It saves time. Creating a new model from scratch is a labor-intensive process. It requires a lot of effort to select the right architecture and parameters, design a trainable model, test it for overfitting, and then tweak the weights until it has good performance.
Using a pre-trained model significantly cuts down on this time. It improves learning. When a pre-trained model is used to learn a new task, the rate of improvement of skill during training is faster and higher than it would be without transferring knowledge from the existing model. It also helps the model to converge to a better solution faster than it would otherwise. This is similar to how a person who has some experience riding a bicycle can ride one more quickly than someone who doesn’t. There are several types of transfer learning, and they are broadly classified based on the nature of the source domain and target task. Some examples include: Heterogeneous transfer learning tries to leverage the knowledge gained in multiple domains to solve cross-domain tasks.
For example, it uses the knowledge of a deep learning model that understands linguistic structures to improve its performance in other tasks such as next word prediction, question-answering and machine translation. Feature-based transfer learning aims to find a common feature representation in the source and target domains. This can be asymmetric or symmetric. Asymmetric approaches transform the original features to match the target domain, while symmetric approaches discover a common latent feature space and then learn to extract the relevant features. Heterogeneous transfer learning is widely used in NLP and image classification tasks, including text-to-text classification, object recognition and more. It is a popular method for reducing the time to learn new tasks and improving their accuracy.
