How to Visually Explain any CNN based Models. Understand and Implement Guided Grad CAM to visually explain class discriminative visualization for any CNN based models
CNN Deep Learning Models — Why it interpreted what it interpreted?
Deep Learning models are now able to give very high accuracy. The most critical piece for adopting computer vision algorithms at scale for Image Classification, Object Detection, Semantic Segmentation, Image Captioning, or Visual Question-Answer is understanding why the CNN model interpreted what they interpreted.
Explainability or Interpretability of a CNN model is the key to build the trust and its adoption
Only if we understand why the model failed to identify a class or an object, then we can concentrate our efforts to address the failure, of the model. Better explainable or interpretable deep learning models will help humans build trust and lead to higher adoption rates.
A good explainable or interpretable model should highlight fine-grained details in the image to visually explain why a class was predicted by the model.
Several methods explain the CNN models like
Guided Grad CAM combines the best of Grad CAM, which is class-discriminative and localizes relevant image regions, and Guided Backpropagation, which visualizes gradients with respect to the image where negative gradients set to zero to highlight import pixel in the image when backpropagating through ReLU layers.
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Explainable AI is more important for those processes where understanding the process of getting prediction is more important than just getting higher accuracy.
Key Aspects of Machine Learning Operations, Explained. If you have ever worked or currently working in the IT field, then you definitely faced the common term «machine learning.
Machine Learning is an utilization of Artificial Intelligence (AI) that provides frameworks the capacity to naturally absorb and improve as a matter of fact without being expressly modified. AI centers round the improvement of PC programs which will get to information and use it learn for themselves.The way toward learning starts with perceptions or information, for instance , models, direct understanding, or guidance, so on look for designs in information and choose better choices afterward hooked in to the models that we give. The essential point is to allow the PCs adapt consequently without human intercession or help and modify activities as needs be.