Welcome to the Rohde und Schwarz Competition repository dedicated to the interpretation of activation maps in generative models!
Recent advancements in Explainable AI and Computer Vision have led to improved interpretability of Convolutional Neural Network (CNN) architectures. Techniques such as Class Activation Mapping (CAM), Grad-CAM, and Guided Grad-CAM have demonstrated the practicality of localized visual attention in classification and categorization tasks. However, limited research has been conducted on applying these methods to generative models.
In this project, we implement the Grad-CAM technique on Variational Autoencoder (VAE) and Conditional Variational Autoencoder (CVAE) models trained on the CelebA-HQ dataset. We aim to generate controllable human faces and perform semantic segmentation on these faces. Subsequently, we explore methodologies to enhance explainability by applying Explainable AI techniques like Grad-CAM. Additionally, we analyze the effects of modifying model architectures, loss functions, and latent space sizes.
The repository contains implementations of VAE and CVAE models trained on the CelebA-HQ dataset. We provide code for generating human faces, performing semantic segmentation, and applying Grad-CAM for interpretability analysis.
Detailed documentation is provided to explain the implementation details, model architectures, and methodologies used for interpretability analysis.
We present the results of our experiments and analysis, including visualizations of activation maps, comparisons between different model configurations, and insights into latent space exploration.
Feel free to explore the contents of this repository to understand our approach to interpreting activation maps in generative models. You can:
Examine the implementation details and methodologies used in our experiments.
Experiment with different model configurations, loss functions, and latent space sizes to observe their effects on model performance and interpretability.
If you have insights, suggestions, or improvements to share, we welcome contributions from the community. Please feel free to fork this repository, make your changes, and submit a pull request.
To learn more about Explainable AI, Generative Models, and Computer Vision, consider exploring the following resources:
Your feedback is valuable to us! If you have any questions, suggestions, or feedback regarding our project, please don't hesitate to reach out. We're eager to collaborate and improve our understanding of interpretability in generative models.
This repository is licensed under the MIT License.
Thank you for your interest in our project! 🌟
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