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 Transfer Learning Strategies
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- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, 2012 Advances in Neural Information Processing Systems 25 (Curran Associates, Inc.) - This paper introduced AlexNet, a deep convolutional neural network that won the ImageNet Large Scale Visual Recognition Challenge in 2012, boosting the use of CNNs and pre-training on large datasets for transfer learning.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - A widely recognized textbook that provides a theoretical and practical foundation for deep learning, including detailed discussions on transfer learning, fine-tuning, and feature extraction.
- CS231n: Convolutional Neural Networks for Visual Recognition, Lecture Notes, Fei-Fei Li, Justin Johnson, Serena Yeung, et al., 2017 (Stanford University) - Provides accessible explanations of CNN architectures, training techniques, and practical applications, including sections on transfer learning strategies such as feature extraction and fine-tuning.
- Deep Transfer Learning for Computer Vision: A Survey, Fuzhen Zhuang, Zhiyuan Luo, Huaxiu Lei, Fei Xu, Yong Xie, Qing He, 2020 ACM Computing Surveys, Vol. 53 (ACM) DOI: 10.1145/3371607 - This survey offers a comprehensive overview of recent advancements in deep transfer learning techniques specifically for computer vision tasks, covering fundamental strategies like feature extraction and fine-tuning as well as more advanced methods.