Common Machine Learning Implement Deep Neural Network Classifier Case Study Engineering Assignment Help

Assignment Task

Overview
You will implement a deep neural network classifier to predict whether two images belong to the same class.
The approach you are asked to follow is quite generic and can be applied to problems where we seek to determine whether two inputs belong to the same equivalence class.
You will write code, perform experiments and report on your results.
Introduction
A neural network can be trained to learn equivalence relations between objects. The core idea is to learn an embedding function such that equivalent objects are mapped to close points and nonequivalent objects are mapped to points that are far apart. These neural networks are called Siamese neural networks because they are used in tandem on two different input vectors. Applications of Siamese networks range from recognizing handwritten checks, automatic detection of faces in camera images, animal in the wild re-identification and matching queries with indexed documents. In this assignment, you will design a Siamese network to predict whether two glyphs belong to the same alphabet. 

Background
Common machine learning tasks like classification and recognition involve learning an appearance model. These tasks can be interpreted and even reduced to the problem of learning manifolds from a training set. Useful appearance models create an invariant representation of the objects of interest under a range of conditions. A good representation should combine invariance and discriminability. For example, in facial recognition where the task is to compare two images and determine whether they show the same person, the output of the system should be invariant to the pose of the heads. More generally, the category of an object contained in an image should be invariant to viewpoint changes. This assignment borrows ideas from a system we developed for a manta ray recognition system. The motivation for our research work was the lack of fully automated identification systems for manta rays. The techniques developed for such systems can also potentially be applied to other marine species that bear a unique pattern on their body. The task of recognizing manta rays is challenging because of the heterogeneity of photographic conditions and equipment used in acquiring manta ray photo ID images like those in the figures below.  Two images of the same Manta ray Many of those pictures are submitted by recreational divers.

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