How can transfer learning be leveraged to improve the performance of machine learning models in domains with limited labeled data, and what techniques can be used to adapt pre-trained models from a source domain to a significantly different target domain without suffering from negative transfer effects?
mrithul sachinBegginer
Transfer learning is a smart way to make your machine learning model better, especially if you don’t have a lot of labeled data. Here’s how it works:
By using these methods, you can make your model perform well even if you don’t have a lot of data from the new domain.
Transfer learning tackles limited data by leveraging a pre-trained model from a large, general source domain. This model extracts valuable features like image recognition basics that generalize well. These features are then fine-tuned on the smaller target dataset, requiring less training and boosting target task accuracy.
Adapting models across very different domains is crucial to avoid negative transfer (source biases hurting target performance). Here are two techniques:
Fine-tuning: Freeze the pre-trained layers (general features) and only train the final layers on the target task. This balances leveraging general knowledge with adapting to the specific target domain.
Domain Adaptation: Techniques like adversarial training or discrepancy minimization aim to align the model’s understanding of both domains. This mitigates negative transfer and improves target domain performance.
In domains with limited labeled data, transfer learning boosts model performance by leveraging pre-trained knowledge. A pre-trained model on a large, general dataset (source domain) acts as a teacher, extracting valuable features that apply to the target domain. These features are then fine-tuned on the limited target data, requiring less training and improving accuracy.
However, adapting models from very different domains is tricky. To avoid negative transfer, where the source biases hurt target performance, we can: