The Basics of Transfer Learning
Transfer learning is basically reusing a pre-trained model on a new problem. In deep learning it is very common at the moment because it can train deep neural networks with relatively less data. In the field of data science, this is very helpful since many real-world problems generally do not have numerous labeled data points to train such complicated models. The knowledge of an already trained machine learning model is applied to a different but related issue in transfer learning. For instance, if you trained a simple classifier to predict whether an image contains a table, you could use the knowledge that the model gained to recognize other objects such as a lamp during its training. We basically attempt to exploit what has been learned in one task with the help of transfer learning to enhance generalization in another. We transfer the weights that our network learned at “task X” to a new “task Y”. Transfer learning is primarily used because of the immense amount of computing resources used in computer vision and natural language processing areas like sentiment analysis.
Transfer Learning in Computer Vision:
Transfer learning is commonly used methodology in computer vision because it helps us to create realistic models in a time-efficient manner. With transfer learning, you start from patterns that have been learned while solving a different problem instead of beginning the learning process from scratch. You take advantage of prior learning in this way to prevent starting from scratch.
Transfer learning is commonly expressed in computer vision by the use of pre-trained models. A pre-trained model is a model trained on a large dataset in order to resolve a problem similar to one that we want to solve.
When it comes to problems in deep learning that require rigorous training like computer vision and natural language processing, transfer learning can be extremely useful.
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Ashpreet Kaur - Jul 2, 2021
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