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What is Transfer Learning ?
Transfer learning is a research topic in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although practical ties between the two fields are limited. From the practical standpoint, reusing or transferring information from previously learned tasks for the learning of new tasks has the potential to significantly improve the sample efficiency of a reinforcement learning agent.
Some of Pretrained models which are used for Transfer Learning
ResNet
MobileNet
Xception
InceptionResNetV2
Densenet
Facenet
AlexNet
Vgg16-19
GoogleLetNet
SqueezeNet
InceptionV3
When to use Transfer Learning
It is an optimization, to save time or get better performance. Three possible benefits to look for when using transfer learning.
Higher start
Higher Slop
Higher Asymptote
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