The document provides an in-depth overview of Residual Networks (ResNet), emphasizing its importance in deep learning and addressing common challenges like vanishing gradients. It explains key concepts such as skip connections, residual learning, and architecture components, highlighting the effectiveness of ResNet in various applications within computer vision. Finally, it discusses implementation in PyTorch, performance metrics, and future directions for research in residual learning.

1. Understanding ResNet (Residual
Networks)
• A Deep Dive into One of the Most Influential
Architectures in Deep Learning

2. What are CNNs?
• Convolutional Neural Networks are the
cornerstone of deep learning in computer
vision.
• Use convolutional layers to extract features.
• Pooling layers to reduce dimensions.
• Fully connected layers for classification.

3. Why Deep Networks?
• Deeper networks can learn more complex
patterns but come with challenges.
• Can model intricate features of data.
• However, prone to issues like vanishing
gradients.

4. Challenges in Deep Learning
• Deep neural networks often face issues like
vanishing and exploding gradients.
• Degraded performance as depth increases.
• Higher computational costs.

5. Understanding Vanishing Gradients
• Gradients become too small, causing slow or
stalled training.
• Occurs mainly in networks with Sigmoid
activation.
• Gradients shrink as they backpropagate.

6. ResNet to the Rescue!
• Residual Networks (ResNet) are designed to
mitigate vanishing gradients.
• Introduces shortcut connections.
• Allows for training of very deep networks.

7. Key Idea: Skip Connections
• Skip connections bypass one or more layers,
allowing gradients to flow uninterrupted.
• Simplifies learning when the optimal function
is identity.
• Improves gradient flow.

8. Residual Learning
• The residual function is defined as F(x) = H(x) -
x, making optimization easier.
• ResNet learns this residual function F(x).
• Efficient training and convergence.

9. The Residual Block
• A typical residual block consists of two layers
with a skip connection.
• Each layer includes convolution, batch
normalization, and ReLU.
• Skip connection adds the input directly to the
output.

10. Mathematical Perspective
• The block learns F(x) and outputs H(x) = F(x) +
x.
• This stabilizes training in deep networks.
• Reduces vanishing gradient problem.

11. Why It Works
• Skip connections prevent vanishing gradients
by enabling easier gradient flow.
• Improves convergence in deep networks.
• Simplifies learning identity mappings.

12. Core Components of ResNet
• ResNet architecture includes convolution,
batch normalization, and ReLU layers.
• Convolutional layers for feature extraction.
• Batch normalization for faster training.
• ReLU activation for non-linearity.

13. Batch Normalization
• Normalization helps improve stability and
accelerates convergence.
• Keeps activations in a consistent range.
• Reduces internal covariate shift.

14. ReLU Activation
• Rectified Linear Unit (ReLU) introduces non-
linearity, aiding in training deep networks.
• Activates neurons selectively, avoiding
vanishing gradients.
• Simple and effective for deep architectures.

15. The Shortcut Connection
• Adds the input of a block directly to its output,
facilitating efficient gradient flow.
• Backbone of ResNet’s architecture.
• Enables easier training of deeper networks.

16. ResNet Variants Overview
• ResNet architectures include 18, 34, 50, 101,
and 152 layers.
• Shallower networks (ResNet18) use basic
blocks.
• Deeper networks like ResNet50 use bottleneck
blocks.

17. Bottleneck Blocks in Deeper
ResNets
• Bottleneck blocks improve efficiency in deep
architectures.
• Consist of 1x1, 3x3, and 1x1 convolutions.
• Reduce computational complexity while
increasing depth.

18. ResNet50 Architecture
• ResNet50 is a deeper version with 50 layers,
offering better performance on complex tasks.
• Uses bottleneck layers to increase depth.
• Effective on datasets like ImageNet.

19. Performance Metrics
• ResNet achieved state-of-the-art results on
benchmarks like ImageNet.
• Improves accuracy and reduces error rates.
• Performs well compared to other deep
architectures.

20. Applications of ResNet
• Used widely in computer vision tasks such as
image classification, object detection, and
facial recognition.
• Medical imaging and autonomous vehicles.
• Research and commercial applications.

21. Real-World Impact
• ResNet powers modern AI applications,
impacting fields like medical diagnosis and
self-driving cars.
• Enhanced image analysis and diagnosis.
• Advancements in autonomous vehicle
technology.

22. Implementing ResNet in PyTorch
• Steps to build ResNet models in PyTorch.
• Define residual block class.
• Stack blocks to build deeper networks.
• Use pretrained models for prototyping.

23. Sample PyTorch Code
• Example code for implementing ResNet with
BasicBlock and network definition.
• Explanation of BasicBlock.
• Details of network structure.

24. Training and Fine-Tuning
• Best practices for training ResNet models.
• Use data augmentation for better
generalization.
• Leverage transfer learning.

25. Advanced Topics: ResNeXt and
Beyond
• Extensions like ResNeXt build on ResNet's
principles with grouped convolutions.
• Flexible and scalable architectures.
• Improved efficiency.

26. Future of Residual Learning
• Research is exploring better skip connection
designs and attention mechanisms.
• Enhancing residual networks with
transformers.
• Optimizing for speed and efficiency.

27. Limitations of ResNet
• While powerful, ResNet models can be
computationally expensive and require careful
tuning.
• High computational requirements for deep
models.
• Challenges in hyperparameter optimization.

28. Conclusion
• ResNet transformed deep learning by making
ultra-deep networks feasible, impacting
various fields.
• Revolutionized image classification.
• Inspiration for new architectures.

29. Q&A
• Thank you for your attention! Feel free to ask
questions on any part of the presentation.