Occlusion in Practice with Python and Captum | XAI for Computer Vision скачать в хорошем качестве

Occlusion in Practice with Python and Captum | XAI for Computer Vision

🚀 Course 🚀 Free: https://adataodyssey.com/xai-for-cv/ Paid: https://adataodyssey.com/courses/xai-... In this lesson, we’ll implement occlusion using Python and the Captum library, a powerful tool for model interpretability in PyTorch. We'll apply this method to a deep learning model used to control an automated car. By generating saliency maps, we can visualize which pixels in the input image influence the car’s steering decisions as it navigates a track. In this video, we will: Implement occlusion-based saliency maps using Captum Apply the method to a real-world computer vision task involving autonomous driving Explore how different parameters in the occlusion process can affect the results Discuss how poorly chosen occlusion values can introduce bias and mislead interpretation This is a practical, hands-on example of explainable AI (XAI) and machine learning interpretability in action — perfect for anyone working with deep learning models in vision-based applications. 🚀 Useful playlists 🚀 XAI for CV:    • XAI for CV   XAI:    • Explainable AI (XAI)   SHAP:    • SHAP   Algorithm fairness:    • Algorithm Fairness   🚀 Get in touch 🚀 Medium:   / conorosullyds   Bluesky: https://bsky.app/profile/conorosullyd... Threads: https://www.threads.net/@conorosullyds Website: https://adataodyssey.com/ 🚀 Chapters 🚀 00:00 Intro 01:05 Loading model and dataset 05:01 Occlusion with Captum 09:53 Alternative saliency maps 12:40 Bias in saliency maps