In many research articles on neural network, I read the term "fine-tuning". As far as I understood, this is not a selection of parameters. Could someone please explain what it exactly means?
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Finetuning means taking weights of a trained neural network and use it as initialization for a new model being trained on data from the same domain (often e.g. images). It is used to:
- speed up the training
- overcome small dataset size
There are various strategies, such as training the whole initialized network or "freezing" some of the pre-trained weights (usually whole layers). The article A Comprehensive guide to Fine-tuning Deep Learning Models in Keras provides a good insight into this. Also have a look at the following threads:
