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Advanced CNNs for Computer Vision Applications

Chapter 1: Revisiting CNN Foundations and Modern Architectures

Brief Review of CNN Building Blocks

Evolution of CNN Architectures: AlexNet to ResNet

Understanding Residual Connections and Skip Architectures

Inception Modules and Network-in-Network Concepts

DenseNet: Architecture and Connectivity Patterns

EfficientNet: Compound Scaling for Models

Architectural Design Choices and Trade-offs

Implementing Modern Architectures Practice

Chapter 2: Advanced Training and Optimization Techniques

Advanced Optimization Algorithms

Learning Rate Schedules and Cyclical Learning Rates

Regularization Revisited: Advanced Techniques

Batch Normalization Internals and Alternatives

Weight Initialization Strategies for Deep Networks

Gradient Clipping and Gradient Flow Mitigation

Mixed Precision Training Fundamentals

Debugging and Monitoring Deep CNN Training

Hands-on Practical: Implementing Advanced Training Loops

Chapter 3: Object Detection Algorithms

Two-Stage Detectors: R-CNN Family

Region Proposal Networks Explained

Single-Stage Detectors: YOLO Family

Single-Stage Detectors: SSD and RetinaNet

Anchor Boxes: Design and Refinement

Non-Maximum Suppression Variants

Evaluation Metrics for Object Detection

Implementing an Object Detector Practice

Chapter 4: Image Segmentation Techniques

Semantic Segmentation vs. Instance Segmentation

Fully Convolutional Networks for Segmentation

Encoder-Decoder Architectures: U-Net and SegNet

Dilated (Atrous) Convolutions for Segmentation

DeepLab Family: Atrous Spatial Pyramid Pooling

Instance Segmentation Approaches (Mask R-CNN)

Evaluation Metrics for Segmentation

Hands-on Practical: Building a Semantic Segmentation Model

Chapter 5: Attention Mechanisms and Transformers in Vision

Self-Attention Mechanisms in CNNs

Non-local Neural Networks

Introduction to Vision Transformers

ViT Architecture: Patches, Embeddings, Transformer Encoder

Hybrid CNN-Transformer Models

Comparing CNNs and Transformers for Vision Tasks

Implementing Attention Blocks in CNNs Practice

Chapter 6: Advanced Transfer Learning and Domain Adaptation

Revisiting Transfer Learning Strategies

Fine-tuning vs. Feature Extraction: Advanced Considerations

Adapting Models to Different Data Distributions

Domain Generalization Concepts

Few-Shot Learning with CNNs

Self-Supervised Learning Pre-training for Vision

Hands-on Practical: Fine-tuning Models on Specialized Datasets

Chapter 7: Generative Adversarial Networks for Image Synthesis

GAN Fundamentals Revisited

Challenges in Training GANs

Deep Convolutional GANs (DCGANs)

Conditional GANs for Controlled Generation

StyleGAN Architecture and Style-Based Generation

Evaluation Metrics for GANs

Implementing a DCGAN for Image Generation Practice

Chapter 8: Model Compression and Efficient Deep Learning

Motivation for Efficient Models

Network Pruning Techniques

Knowledge Distillation Methods

Quantization: Reducing Model Precision

Designing Efficient Architectures

Neural Architecture Search Overview

Hands-on Practical: Applying Pruning and Quantization

Revisiting CNN Foundations and Modern Architectures

This chapter starts by reinforcing the essential building blocks of Convolutional Neural Networks (CNNs). We assume familiarity with basic concepts but will briefly review components like convolutional layers, pooling operations (e.g., $max-pooling$ , $average-pooling$ ), and activation functions (e.g., $ReLU(x) = max(0, x)$ ).

Building on this foundation, we examine the progression of CNN architectures. We trace the development from early influential models towards contemporary designs such as ResNet, Inception, DenseNet, and EfficientNet. Key innovations will be analyzed, including:

Residual connections, often represented as $y = \mathcal{F}(x) + x$
Network-in-Network principles
Dense connectivity patterns
Compound scaling strategies

You will gain an understanding of the design choices behind these architectures and the trade-offs involved, considering factors like computational cost and parameter efficiency. The chapter concludes with practical guidance and exercises on implementing these models using standard deep learning frameworks.

Sections

1.1 Brief Review of CNN Building Blocks
1.2 Evolution of CNN Architectures: AlexNet to ResNet
1.3 Understanding Residual Connections and Skip Architectures
1.4 Inception Modules and Network-in-Network Concepts
1.5 DenseNet: Architecture and Connectivity Patterns
1.6 EfficientNet: Compound Scaling for Models
1.7 Architectural Design Choices and Trade-offs
1.8 Implementing Modern Architectures Practice

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