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What is transfer learning – Explain its types and working?

Transfer learning is a machine learning technique that enhances a model’s performance on related tasks by training it for a particular job. Once a pre-trained model has learned features from one dataset, transfer learning enables it to be adjusted on another, related dataset with less training data.

The idea behind transfer learning is to apply the knowledge gained from mastering one activity to another that is related to it. Acquiring the new task could be faster and more accurate. Pre-trained models are frequently trained using a specific architecture on a large dataset. And for a similar task, the learned weights are subsequently used as starting weights for training a new model on a large dataset.

For a different classification task, such as identifying specific item categories in retail photos or diagnosing medical photos, a pre-trained image classification model could be enhanced with a new dataset.

When there are few labeled data available for a new assignment, transfer learning might be especially useful. Training a new model can be easier and faster if a pre-trained model replaces the need for a large amount of labeled data.

Transfer learning is a powerful method for improving model performance on various tasks and has been widely applied in speech recognition, computer vision, and natural language processing.

How transfer learning works?

In the machine learning process known as transfer learning, a pre-trained model is used as a starting point for a new job, enabling more accurate and rapid model development. The pre-trained model was trained on a sizable dataset in the past, revealing significant features that could be applied to new data.

transfer learning

Transfer learning works in the following ways:

  1. Pre-training: A large dataset is used to train a model for a specific task, such as object recognition. This model becomes more adept at identifying patterns and other aspects of the data that are relevant to the task at hand.
  2. Fine-tuning: To perform a different task, such as differentiating between different flower varieties, the previously trained model is then modified to a new, smaller dataset. The pre-trained model is “fine-tuned” to work better on the new task by adjusting its parameters on the new dataset.
  3. Feature extraction: Instead of fine-tuning the entire pre-trained model, in some situations, only the feature extraction layers of the model are used for a new task. This involves taking advantage of the previously trained model to extract useful features from new data, which can then be fed into a newly trained model specifically for the new task.

Transfer learning allows for the reuse of pre-existing models instead of starting from scratch, which has the benefit of saving time and money. It might also improve model performance by utilizing the information from the large pre-training dataset.

What are the four types of transfer learning?

Instance Transfer:

  • This kind involves using instances—that is, data points or examples—directly from the source task to the target task.
  • Without changing the model’s parameters, the knowledge acquired from the source task is applied to the target task.

Feature Representation Transfer:

  • Feature representation transfer involves extracting pertinent features for the target task using the knowledge gained from the source task.
  • Often, the model’s first layers—which capture broad characteristics—are carried over, with the later layers being customized for a given task.

Parameter Transfer:

  • Transferring all or some of the model’s parameters from the source task to the target task is known as parameter transfer.
  • During training on the target task, the weights of the pre-trained model are modified to accommodate the unique features of the fresh data.

Domain Transfer:

  • Domain transfer is the process of moving knowledge to a new target domain from a source domain (such as a particular dataset or environment).
  • Dealing with disparities in data distribution between the source and target domains presents a challenge in domain transfer.

These transfer learning approaches are not exclusive of one another, and based on the particulars and demands of the tasks at hand, a mix of them may be employed. Transfer learning has demonstrated to be especially helpful in situations where there is a lack of labeled data available for the target task, since insights from a related source task can enhance performance.


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