/Transfer-Learning-on-VGG16

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Transfer-Learning-on-VGG16

In deep learning, transfer learning is a technique whereby a pretrained neural network model is used on a problem that is being solved previously.

Depending on both:

The size of the new data set, and The similarity of the new data set to the original data set The approach for using transfer learning will be different. There are four main cases:

  1. New data set is small, new data is similar to original training data.
  2. New data set is small, new data is different from original training data.
  3. New data set is large, new data is similar to original training data.
  4. New data set is large, new data is different from original training data.

Pre-Training

When we train the network on a large dataset(for example: ImageNet) , we train all the parameters of the neural network and therefore the model is learned. It may take hours on your GPU.

Fine Tuning

We can give the new dataset to fine tune the pre-trained CNN. Consider that the new dataset is almost similar to the orginal dataset used for pre-training. Since the new dataset is similar, the same weights can be used for extracting the features from the new dataset.

The previous layers can help to extract the features of the new data. So it will be good if we fix the earlier layers and retrain the rest of the layers, if we got only small amount of data. If you have large amount of data, you can retrain the whole network with weights initialized from the pre-trained network.

VGG 16

VGG16 (also called OxfordNet) is a convolutional neural network architecture.

It was used to win the ILSVR (ImageNet) competition in 2014

The unique thing about VGG16 is that instead of having a large number of hyper-parameter they focused on having convolution layers of 3x3 filter with a stride 1.

It always uses same padding and maxpool layer of 2x2 filter of stride 2.

The 16 in VGG16 refers to it has 16 layers that have weights. This network is a pretty large network and it has about 138 million (approx) parameters. It follows this arrangement of convolution and max pool layers consistently throughout the whole architecture. In the end it has 2 FC(fully connected layers) followed by a softmax for output.