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Transfer learning has become so handy for computer vision geeks.
It’s basically a mechanism where the knowledge acquired by training a model for achieving a task is efficiently modified or optimized in order to accomplish the second related task.
For example, a neural network trained on object recognition can be used to read x-ray scans.
This is achieved by freezing the weights until the initial or mid-layers are learned on the data for task A, removing the last layer or a few of the last layers, and adding new layers and training those parameters using the data for task B.
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