"Transfer learning assumes a pivotal position in the realm of AI, revolutionizing the development and optimization of AI models.
Its contributions can be categorized into three fundamental areas: optimizing AI model efficiency, surmounting data scarcity, and enhancing model generalization..."
"Transfer learning is a technique that utilizes a trained model's knowledge to learn another set of data. Transfer learning aims to improve learning in the target domain by leveraging knowledge from the source domain and learning task. Different transfer learning settings are defined based on the type of task and the nature of the data available in the source and target domains...
In the field of machine learning, transfer learning outperformed state-of-the-art methods. Improved performance in computer vision, in particular, prompted the use of deep learning in computer-based diagnosis and prediction. Transfer learning has gained considerable importance since it can work with little or no information in the training phase. That is, data that are well established are adjusted by move learning from one domain to another. Transfer learning is well suited to scenarios where a version performs poorly due to obsolete data or scant..."
https://www.scribbledata.io/blog/transfer-learning-in-ai-a-complete-guide/
https://www.sciencedirect.com/topics/computer-science/transfer-learning
https://builtin.com/data-science/transfer-learning
Dear Doctor
"Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks."
Sundus F Hantoosh, please add the following link to the source which you were used in your answer. Thanks.
https://en.wikipedia.org/wiki/Transfer_learning
Transfer learning, and Meta-learning are two approaches that enable models to transfer knowledge from one task to another, leading to better generalization and faster training times.
Transfer learning refers to the technique of utilizing the knowledge gained by a machine learning model on one task and applying it to a different, yet related task. The key premise is that the source and target tasks, though not identical, share some commonalities or patterns that could aid the learning process for the new task. This allows models to learn faster, using fewer data and resources than training a model from scratch. For example, image classifiers trained on large datasets can transfer learning capabilities to new image recognition tasks.
Meta-learning, or "learning to learn", aims to improve the model's ability to utilize its previous learnings more quickly for new learning tasks. It focuses on acquiring knowledge about how different machine learning techniques generalize across a distribution of tasks. Essentially, the model is trained on a variety of learning tasks, so that it learns the optimal learning algorithm or initialization itself that allows efficient knowledge transfer to unseen tasks in the future. Automated hyperparameter tuning and few-shot image classification are some example use cases.
The motivation behind transfer learning is to reduce the amount of data and computational resources required to train a model from scratch. By leveraging the pre-trained knowledge of a model, transfer learning allows us to train models on smaller datasets, or with fewer training iterations, and still achieve good performance on the target task. This is particularly useful in scenarios where data is limited or where training from scratch is prohibitively expensive.
Pre-trained models such as VGG, ResNet, and Inception have been trained on large datasets such as ImageNet, and can be fine-tuned for specific tasks with smaller datasets. For example, a model trained on ImageNet can be fine-tuned on a few hundred images of cats and dogs to classify new images of cats and dogs with high accuracy. TensorFlow Hub, Hugging Face Transformers, and Gensim are few popular libraries that provide pre-trained models. Pre-trained models are available for machine learning tasks such as image classification, sentiment analysis, recommendation system, text generation,Text Classification, Text Summarization, Language Translation, Speech Recognition, Music Generation, and so on.
Transfer learning is a machine learning technique where a model trained on one task is adapted for a different but related task. It leverages knowledge gained from solving one problem to help improve performance on a new, possibly more complex, task. Transfer learning is useful in AI because it allows models to benefit from previously learned features, enabling faster and more efficient training for new tasks, especially when labeled data for the new task is limited.
Transfer learning and few-shot learning: Improving generalization across diverse tasks and domains
"Transfer learning and few-shot learning are powerful techniques that enable AI models to leverage pre-existing knowledge and adapt to new scenarios, unlocking the potential for rapid learning and versatile applications in modern AI...
Benefits of Transfer Learning:
https://syedabis98.medium.com/transfer-learning-and-few-shot-learning-improving-generalization-across-diverse-tasks-and-domains-a743781ee357
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