AL3502 Deep Learning for Vision Syllabus:
AL3502 Deep Learning for Vision Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES:
To introduce basic computer vision concepts
To understand the methods and terminologies involved in deep neural network
To impart knowledge on CNN
To introduce RNN and Deep Generative model
To solve real world computer vision applications using Deep learning.
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 VISUALIZATION AND UNDERSTANDING CNN
Convolutional Neural Networks (CNNs): Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG. Visualization of Kernels; Backprop-to-image/ Deconvolution Methods; Deep Dream, Hallucination, Neural Style Transfer; CAM, Grad-CAM.
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
45 PERIODS
PRACTICAL EXERCISES: 30 PERIODS
1. Implementation of basic Image processing operations including Feature Representation and Feature Extraction 2. Implementation of simple neural network
3. Study of pretrained deep neural network model for Images
4. CNN for Image classification
5. CNN for Image segmentation
6. RNN for video processing
7. Implementation of Deep Generative model for Image editing
TOTAL:75 PERIODS
COURSE OUTCOMES:
Upon successful completion of this course, students will be able to:
CO 1: Implement basic Image processing operations
CO 2: Understand the basic concept of deep learning
CO 3: Design and implement CNN and RNN and Deep generative model
CO 4: Understand the role of deep learning in computer vision applications.
CO 5: Design and implement Deep generative model
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.
REFERENCES
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/