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
Region Proposal Networks Explained
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- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, 2015 Advances in Neural Information Processing Systems (NIPS) DOI: 10.48550/arXiv.1506.01497 - The original paper introducing the Region Proposal Network (RPN), which is central to this section. It details the architecture, training, and integration of RPNs into the Faster R-CNN framework.
- Fast R-CNN, Ross Girshick, 2015 Proceedings of the IEEE International Conference on Computer Vision (ICCV) DOI: 10.48550/arXiv.1504.08083 - This paper presents the Fast R-CNN architecture, which significantly improved the training and inference speed of R-CNN by sharing computations across proposals. RPNs were later developed to replace its external proposal generation step.
- Rich feature hierarchies for accurate object detection and semantic segmentation, Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, 2014 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.48550/arXiv.1311.2524 - The foundational work that established the two-stage object detection paradigm using convolutional neural networks and external region proposals. It provides essential context for the evolution leading to RPNs.