CSE 676:Deep Learning - Neural Networks - IT/Computer Science Assignment Help

Assignment Task:

Task:

Introduction to Deep Learning Neural Networks Many deep learning models have been proposed and implemented towards the task of ex-
tracting features from a given image for classification. There is a lot of literature discussing new architectures from the point of view of the layers composition and recognition perfor- mance. Hence, it is very informative as an introductory project to analyze the aspects of a few architectures in terms of memory usage and inference time and how computational cost impacts the recognition accuracy.
 

2 Networks
2.1 VGGNet [1]
The VGG network architecture was introduced by Simonyan and Zisserman in their 2014 paper, Very Deep Convolutional Networks for Large Scale Image Recognition. This network is characterized by its simplicity, using only 3×3 convolutional layers stacked on top of each other in increasing depth. Reducing volume size is handled by max pooling. Two fully-connected layers, each with 4,096 nodes are then followed by a softmax classifier.
 

2.2 ResNet [1]
Unlike traditional sequential network architectures such as AlexNet, OverFeat, and VGG, ResNet is instead a form of “exotic architecture” that relies on micro-architecture modules (also called “network-in-network architectures”).

Task Definition
1. Implement VGGNet 16, ResNet 18, InceptionV2 architectures using SGD[2] and ADAM[3] optimization with the following variations in network regularization schemes:
(a) No Regularization
(b) Batch Normalization [4]
(c) Dropouts [5]
2. Use CIFAR-100 dataset for training and testing.
3. Calculate Precision, Recall and Accuracy to evaluate each of the 18 experiments.
4. Implement Early Stopping regularization scheme in all the experiments.

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