Update README.md

Author: anujshah1003

README.md

@@ -8,3 +8,24 @@ Definition : Given a source domain Ds and a learning task Ts, a target domain Dt
 
 A good explanation of how to use transfer learning practically is explained in http://cs231n.github.io/transfer-learning/
 
+When and how to fine-tune? How do you decide what type of transfer learning you should perform on a new dataset?
+This is a function of several factors, but the two most important ones are the size of the new dataset (small or big), and its similarity
+to the original dataset (e.g. ImageNet-like in terms of the content of images and the classes, or very different, such as microscope images).
+Keeping in mind that ConvNet features are more generic in early layers and more original-dataset-specific in later layers, 
+here are some common rules of thumb for navigating the 4 major scenarios:
+
+	New dataset is small and similar to original dataset. Since the data is small, it is not a good idea to fine-tune the ConvNet 
+due to overfitting concerns. Since the data is similar to the original data, we expect higher-level features in the ConvNet to be 
+relevant to this dataset as well. Hence, the best idea might be to train a linear classifier on the CNN codes.
+
+	New dataset is large and similar to the original dataset. Since we have more data, we can have more confidence that we won’t 
+overfit if we were to try to fine-tune through the full network.
+
+	New dataset is small but very different from the original dataset. Since the data is small, it is likely best to only train a 
+linear classifier. Since the dataset is very different, it might not be best to train the classifier form the top of the network, 
+which contains more dataset-specific features. Instead, it might work better to train the SVM classifier from activations somewhere 
+earlier in the network.
+
+	New dataset is large and very different from the original dataset. Since the dataset is very large, we may expect that we can 
+afford to train a ConvNet from scratch. However, in practice it is very often still beneficial to initialize with weights from a 
+pretrained model. In this case, we would have enough data and confidence to fine-tune through the entire network.