How I got my first paper — Part — 1

source: https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQi3gpVEcvUx0LPAtaD1M6Ow8vjq2RN8pfDsQ&usqp=CAU
Mathematical Representation of AN-MRR
Mathematical Representation of Precision
Mathematical Representation of MAP

Deep auto encoder to learn powerful features of all images in the dataset.
Discriminative classification network takes a pair of features, query and target and classifies if both are similar.
Generalization capability of Batch Normalization is better than dropout.
Mainly trained on GTCross View and then fine tuned on other datasets as it contains 1 million pairs having both street and satellite views and that is the reason of using it as a base for training.
Cross Entropy used as a loss for the discriminative network.

References