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The common practice is to truncate the last layer (soft max layer) of the pre-trained network and replace it with our new soft max layer that are relevant to our own problem. Use a smaller learning rate to train the network.
Fine-tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.
Fine-tuning is one approach to transfer learning. In Transfer Learning or Domain Adaptation we train the model with a dataset, and after we train the same model with another dataset that has a different distribution of classes, or even with other classes than in the training dataset).
Transfer learning is when a model developed for one task is reused for a model on a second task. Fine-tuning is one approach to transfer learning, and it is very popular in computer vision and NLP. The most common example given is when a model is trained on Imagine is fine-tuned on a second task.
Fine-tuning is constant monitoring of the economic growth level of a country and intervening with expansionary fiscal policy when it starts to fall (that is, increased government spending) and contractionary fiscal policy when it starts to rise (that is, increased taxation) to ensure over time that there is a
Fine-tuning is a process to take a network model that has already been trained for a given task, and make it perform a second similar task.
: to make small changes to (something) in order to improve the way it works or to make it exactly right. See the full definition for fine-tune in the English Language Learners Dictionary.
Fine-Tuning. Fine-tuning refers to the process of adjustment that brings equilibrium in the economy. The economy is a dynamic one. In these situations, government authorities change some factors so that the economy would reach to the equilibrium level. This process of changes in the factors is known as fine-tuning.
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