Convolutional Neural Network with an Optimized Backpropagation Technique
Abstract: This paper exhibits an Object recognition technique using the Convolutional Neural Network in Deep Learning. Backpropagation is a redundantly used method to calculate the gradient of a curve. The gradient, in turn, is involved in the weight upgradation while training the deep neural network. Being a repeatedly rediscovered algorithm, Backpropagation still stands out to give better results with its various optimization techniques. An Object Recognition technique in deep learning using backpropagation, optimized with a heuristic optimization technique is implemented and evaluated.
Paper Link: https://ieeexplore.ieee.org/document/8878719
(Use Sci Hub in case you are not able to access the paper)
Published in: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)
Tasks:
- Implement the model in Tensorflow. Specify the number of layers of each type for the CNN that you are implementing.
- Only RMSPROP and Adadelta optimizer results need to be shown.
Data Set: http://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php
Paper ID: 52