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
Debugging and Monitoring Deep CNN Training
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This comprehensive textbook covers foundational concepts in deep learning, including optimization, regularization, and common training challenges such as overfitting, underfitting, and gradient issues.
- Deep Residual Learning for Image Recognition, Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, 2016 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2016.90 - Introduces residual networks, an architectural innovation that effectively addresses the vanishing gradient problem in deep neural networks, enabling the training of models with many layers.
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Sergey Ioffe and Christian Szegedy, 2015 Proceedings of the 32nd International Conference on Machine Learning (ICML) - Presents Batch Normalization, a technique to normalize layer inputs across mini-batches, which stabilizes training, speeds up convergence, and mitigates vanishing/exploding gradients.
- On the difficulty of training recurrent neural networks, Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio, 2013 International Conference on Machine Learning (ICML) - A foundational work that details the issues of exploding and vanishing gradients in deep networks and proposes gradient clipping as a method to counter exploding gradients.
- Convolutional Neural Networks for Visual Recognition (CS231n) Lecture Notes, Stanford University (Course Staff), 2023 - These widely recognized online course notes provide practical guidance on training, debugging, and monitoring deep learning models, including discussions on common pitfalls and best practices.