AL3502: Deep Learning for Vision syllabus for AI&ML 2021 regulation

Deep Learning for Vision detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Artificial Intelligence & Machine Learning 5th Sem scheme and its subjects, do visit AI&ML 5th Sem 2021 regulation scheme. The detailed syllabus of deep learning for vision is as follows.

Course Objectives:

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Unit I

COMPUTER VISION BASICS
Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution
Visual Features and Representations: Edge, Blobs, Corner Detection; Visual Features extraction: Bag-of-words, VLAD; RANSAC, Hough transform.

Unit II

INTRODUCTION TO DEEP LEARNING
Deep Feed-Forward Neural Networks – Gradient Descent – Back-Propagation and Other Differentiation Algorithms – Vanishing Gradient Problem – Mitigation – Rectified Linear Unit (ReLU) – Heuristics for Avoiding Bad Local Minima – Heuristics for Faster Training – Nestors Accelerated Gradient Descent – Regularization for Deep Learning – Dropout – Adversarial Training – Optimization for Training Deep Models.

Unit III

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Unit IV

CNN and RNN FOR IMAGE AND VIDEO PROCESSING
CNNs for Recognition, Verification, Detection, Segmentation: CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN. CNNs for Segmentation: FCN, SegNet.
Recurrent Neural Networks (RNNs): Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity Recognition

Unit V

DEEP GENERATIVE MODELS
Deep Generative Models: Review of (Popular) Deep Generative Models: GANs, VAEs
Variants and Applications of Generative Models in Vision: Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security;
Recent Trends: Self-supervised Learning; Reinforcement Learning in Vision;

Practical Exercises

1. Implementation of basic Image processing operations including Feature Representation and Feature Extraction 2. Implementation of simple neural network
2. Study of pretrained deep neural network model for Images
3. CNN for Image classification
4. CNN for Image segmentation
5. RNN for video processing
6. Implementation of Deep Generative model for Image editing

Course Outcomes:

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Text Books:

1. Ian Goodfellow Yoshua Bengio Aaron Courville, “Deep Learning”, MIT Press, 2017
2. Ragav Venkatesan, Baoxin Li, “Convolutional Neural Networks in Visual Computing”, CRC Press, 2018.

Reference Books:

1. Rajalingappaa Shanmugamani ,Deep Learning for Computer Vision, Packt Publishing, 2018
2. David Forsyth, Jean Ponce, Computer Vision: A Modern Approach, 2002.
3. Modern Computer Vision with PyTorch, V.Kishore Ayyadevara, Yeshwanth Reddy, 2020 Packt Publishing Ltd
4. Goodfellow, Y, Bengio, A. Courville, “Deep Learning”, MIT Press, 2016.
5. Richard Szeliski, Computer Vision: Algorithms and Applications, 2010.
6. Simon Prince, Computer Vision: Models, Learning, and Inference, 2012.
7. https://nptel.ac.in/

For detailed syllabus of all other subjects of Artificial Intelligence & Machine Learning, 2021 regulation curriculum do visit AI&ML 5th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.