"[...] transfer learning allows a machine learning model to port the knowledge it has acquired during training to new tasks, extending the reach of the combination of computation and expertise having been used as fuel for the original model. Simply put, transfer learning can save training time and extend the usefulness of existing machine learning models. It is also an invaluable technique for tasks where the large amounts of training data typically required for training a model from scratch are not available." (Dipanjan Sarkar et al, "Hands-On Transfer Learning with Python", 2018)
"Transfer learning has immense potential and is a commonly required enhancement for existing learning algorithms. Yet, there are certain pertinent issues related to transfer learning that need more research and exploration. Apart from the difficulty of answering the questions of what, when, and how to transfer, negative transfer and transfer bounds present major challenges." (Dipanjan Sarkar et al, "Hands-On Transfer Learning with Python", 2018)
"Transfer learning is a machine learning (ML) technique where knowledge gained during the training of one set of ML problems can be used to train other similar types of problems." (Dipanjan Sarkar et al, "Hands-On Transfer Learning with Python", 2018)
"Transfer learning takes the process of learning one step further and more inline with how humans utilize knowledge across tasks. Thus, transfer learning is a method of reusing a model or knowledge for another related task. Transfer learning is sometimes also considered as an extension of existing ML algorithms. Extensive research and work is being done in the context of transfer learning and on understanding how knowledge can be transferred among tasks." (Dipanjan Sarkar et al, "Hands-On Transfer Learning with Python", 2018)
"In a nutshell, transfer learning refers to the machine learning paradigm in which an algorithm extracts knowledge from one or more application scenarios to help boost the learning performance in a target scenario. Compared to tra-ditional machine learning, which requires large amounts of well-defined training data as the input, transfer learning can be understood as a new learning paradigm." (Qiang Yang et al, "Transfer Learning", 2020)
"[...] in machine learning practice, we observe that we are often surrounded with lots of small-sized data sets, which are often isolated and fragmented. Many organizations do not have the ability to collect a huge amount of big data due to a number of constraints that range from resource limitations to organizations inter-ests, and to regulations and concerns for user privacy. This small-data challenge is a serious problem faced by many organizations applying AI technology to their problems. Transfer learning is a suitable solution for addressing this challenge be-cause it can leverage many auxiliary data and external models, and adapt them to solve the target problems." (Qiang Yang et al, "Transfer Learning", 2020)
"[...] transfer learning can make AI and machine learning systems more reliable and robust. It is often the case that, when building a machine learning model, one cannot foresee all future situations. In machine learning, this problem is of-ten addressed using a technique known as regularization, which leaves room for future changes by limiting the complexity of the models. Transfer learning takes this approach further, by allowing the model to be complex while being prepared for changes when they actually come." (Qiang Yang et al, "Transfer Learning", 2020)
"Transfer learning deals with how systems can quickly adapt themselves to new situations, new tasks and new environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available in the target domain. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world." (Qiang Yang et al, "Transfer Learning", 2020)
"Transfer learning (TL) and multi-task learning (MTL) methods reduce the amount of experience needed to train individual task models by reusing knowledge from other related tasks. This transferred knowledge can improve the training speed and model performance, as compared to learning the tasks in isolation following the classical machine learning pipeline. TL and MTL techniques typically select the relevant knowledge to transfer by modeling inter-task relationships using a shared representation, based on training data for each task." (Mohammad Rostami, "Transfer Learning Through Embedding Spaces", 2021)
"The goal of transfer learning is to improve learning quality and speed of the current ML algorithm through overcoming labeled data scarceness, avoiding redundant learning and model retraining, and using computational power resources efficiently. In particular, since deep neural networks are becoming dominant models in machine learning, training complex models with several millions of parameters has become a standard practice which makes model retraining expensive. Transfer learning can be very useful since labeling millions of data points is not practical for many real-world problems." (Mohammad Rostami, "Transfer Learning Through Embedding Spaces", 2021)
"AI is intended to create systems for making probabilistic decisions, similar to the way humans make decisions. […] Today’s AI is not very able to generalize. Instead, it is effective for specific, well-defined tasks. It struggles with ambiguity and mostly lacks transfer learning that humans take for granted. For AI to make humanlike decisions that are more situationally appropriate, it needs to incorporate context." (Jesús Barrasa et al, "Knowledge Graphs: Data in Context for Responsive Businesses", 2021)
"The idea behind transfer learning is that the pre-trained model has already learned a lot of information about the language and relationships between words, and this information can be used as a starting point to improve performance on a new task. Transfer learning allows LLMs to be fine-tuned for specific tasks with much smaller amounts of task-specific data than would be required if the model were trained from scratch. This greatly reduces the amount of time and resources needed to train LLMs." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)
"Transfer learning is a technique used in machine learning to leverage the knowledge gained from one task to improve performance on another related task. Transfer learning for LLMs involves taking an LLM that has been pre-trained on one corpus of text data and then fine-tuning it for a specific 'downstream' task, such as text classification or text generation, by updating themodel’s parameters with task-specific data." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)
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