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
Domain Generalization Concepts
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- Invariant Risk Minimization, Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz, 2020 International Conference on Learning Representations (ICLR 2020) DOI: 10.48550/arXiv.1907.03896 - Introduces Invariant Risk Minimization (IRM), a core method for learning invariant predictors across different training environments, directly addressing the 'Gradient-Based Regularization' approach.
- Domain-Adversarial Training of Neural Networks, Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, Victor Lempitsky, 2016 Journal of Machine Learning Research, Vol. 17 (JMLR) - Introduces domain-adversarial training, a foundational technique for learning domain-invariant features by minimizing domain discriminability, a concept applicable to aligning multiple source domains in DG.