cnn explainer.pptx

cnn explainer reasearch paper slides

1. CNN EXPLAINER INAM UL HAQ 20014119-012

2. CNN EXPLAINER CNN EXPLAINER is an interactive visualization tool designed for beginners to understand Convolutional Neural Networks (CNNs) in deep learning. It offers three integrated views: an overview of the CNN architecture, a detailed examination of neuron activations through sliding kernels, and a view for inspecting the underlying mathematics of convolution.

3. TABLE OF CONTENTS 01 INTRODUCTION 02 BACKGROUND 03 RELATED WORK 04 RESEARCH & DESIGN 05 DESIGN GOALS 06 USAGE SCENARIOS 07 DISCUSSION & FUTURE WORK 08 CONCLUSION

4. 01 INTRODUCTION

5. Understanding CNN Deep learning's success raises interest but poses challenges for beginners. CNNs, a fundamental model, present hurdles in grasping both high-level structure and low-level operations. Beginners struggle to connect unfamiliar layer mechanisms with complex CNN structures. Existing visualization tools focus on either high-level structure or low-level operations, lacking an integrated approach.

6. FEATURES & IMPACT Features: Overview + detail and animation techniques for high-level structure and low-level operations. Fluid transitions between abstraction levels for comprehensive understanding. Open-source, web-based implementation for accessibility without advanced computational resources. Impact: Broadening access to deep learning education with an open-source, web-based implementation. A step towards democratizing and lowering barriers to understanding and applying artificial intelligence technologies.

7. 02 BACKGROUND

8. CNNs for Image Classification Introduction: Objective of supervised image classification: map input image (X) to output class (Y). CNNs demonstrate state-of-the-art performance by learning representations of image data through multiple layers of computation. CNN Layers: Composition of various layers, including convolutional, downsampling , and activation layers. Convolutional layers extract features for classification, with early layers capturing low-level features (e.g., edges) and later layers extracting more complex semantic features (e.g., car headlights).

9. Operations and Optimization Activation Functions: Activation functions (e.g., ReLU ) introduce non-linearity, enabling the model to learn complex patterns. Softmax activation normalizes classification scores, yielding output class probabilities. Advantages of CNNs: Spatially-aware representations are created through stacked layers, contrasting with over-parameterized classic image classification models.

10. 03 RELATED WORK

11. Visualization for DL Education Overview: Various tools like Teachable Machine, Deep Visualization Toolbox, ConvNetJS MNIST demo, TensorFlow Playground, and GAN Lab allow users to interactively train and understand deep neural networks. Current educational resources often focus on either high-level model structures or low-level mathematics, leaving a gap in connecting unfamiliar layer mechanisms with complex model structures.

12. Visualization and Engagement Algorithm Visualization (AV): Preceding deep learning's popularity, AV tools were designed to help learners understand dynamic algorithm behavior. AV's effectiveness in computer science education varies, with student engagement identified as a key factor. CNN EXPLAINER's design draws inspiration from AV guidelines, covering modern machine learning algorithms and advancing the AV landscape..

13. Understanding with Visual Analytics Visual Analytics Tools: Tools like Summit, LSTMVis , and GANVis aid experts in analyzing deep learning models and predictions. DGMTracker and DeepEyes assist developers in understanding the training process of CNNs and GANs. Visual analytics tools also contribute to detecting and interpreting vulnerabilities in deep learning models. CNN EXPLAINER focuses on non-experts, offering an accessible way for learners to understand deep learning concepts through interactive visualization.

14. 04 RESEARCH AND DESIGN

15. Design Challenges in CNN Learning Intricate Model Structure: CNNs involve complex layer structures, often challenging for beginners. Survey results emphasize the need for a visual tool to explain the intricate model construction. Complex Layer Operations: Different layers in CNNs serve distinct purposes, with complex mathematical operations. Students find CNN layer computations challenging, requiring a tool to simplify and explain these operations.

16. Design Challenges and Solutions Connection Between Model Structure and Layer Operation: The interplay between low-level mathematical operations and high-level model structure is crucial. Understanding the translation of equations into a mental model is challenging and requires focused attention. Challenge in Deploying Interactive Learning Tools: Most neural networks are implemented in frameworks like TensorFlow and PyTorch . The challenge is to make CNN understanding accessible without requiring installation and coding skills.

17. 05 DESIGN GOALS

18. Design Objectives and Benefits Visual Summary of CNN Models and Data Flow: Objective: Provide an overview of CNN structure by visualizing layer outputs and connections in a single view. Benefit: Helps users track the transformation of input data to final predictions through layer operations. Interactive Interface for Mathematical Formulas: Objective: Design an interactive interface for each mathematical formula used in CNNs. Benefit: Enables users to examine and understand the intricate mathematical operations behind each layer.

19. Design Objectives and Benefits Clear Communication and Engagement: Objective: Ensure clear communication and engagement through accompanying explanations and customizable visualizations. Benefit: Enhances user interpretation of graphical representations, actively engaging learners in the learning process. Web-Based Implementation: Objective: Develop a web-based tool for accessibility without installation or coding requirements. Benefit: Widens access, enabling users to learn and interact with CNNs directly on laptops or tablets.

20. 06 USAGE SCENARIOS

21. Beginner Learning Layer Connectivity User: Janis, a virology researcher. Objective: Understanding 3D to 1D transformation in CNNs. Journey: Starts from the Overview (Fig. 5A). Explores connections through detailed views. Learns hidden operations via interactive views. Impact: Janis gains insights into often-overlooked operations, feeling more confident in applying CNNs to her research.

22. Teaching Through Interactive Experimentation User: Damian, a university professor teaching computer vision. Objective: Demonstrate convolutional operations and hyperparameters. Classroom Experience: Projects CNN EXPLAINER for a live class demonstration. Engages students in interpreting sliding window animations. Demonstrates impact of altering hyperparameters in real time. Impact: Provides an interactive and dynamic learning experience, allowing students to experiment with concepts in real image inputs.

23. 07 DISCUSSION & FUTURE WORK

24. Discussion and Future Work Explaining Training Process: CNN EXPLAINER's Current Focus: Transforming input data to class prediction. Future Endeavor: Addressing user interest in training process and backpropagation. Approach: Collaborate with instructors and students to design visualizations for detailed understanding.

25. Research and Expansion Generalization to Other Models: Current Success: Effectively explaining CNN layer operations and structure. Future Scope: Adapting Interactive Formula Views to diverse layer types and neural network models (e.g., Leaky ReLU , Residual Block). Best Practices Integration: Current Application: CNN EXPLAINER integrates visualizations with explanations and customizable features (G4). Future Opportunities: Explore additional algorithm visualization (AV) best practices (e.g., interactive quizzes, user-built visualizations).

26. 8 CONCLUSION

27. Bridging Gaps in AI Education Deep Learning Complexity: Deep learning's increasing impact, yet the challenge for beginners. CNN EXPLAINER Solution: An interactive tool simplifying Convolutional Neural Networks (CNNs). Addressing Learning Challenges: Tackles intricate model structures, complex layer operations, and connections between them. Design Goals: Visual summaries, interactive math interfaces, seamless abstraction transitions, clear communication, and web-based accessibility. Real-World Scenarios : Illustrated through a virology researcher's learning journey and a professor's interactive classroom demonstration. Future Directions: Expanding to cover other layer types, integrating algorithm visualization best practices, and quantitative evaluation plans.