Activation Atlas
Exploring Neural Networks with Activation Atlases By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts. Above, an activation atlas of the InceptionV1 vision classification network reveals many fully realize
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
We propose a technique for producing "visual explanations" for decisions from a large class of CNN-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept, flowing into the final convolutional layer to produce a coarse localization map highlighting important regions in the image for predicting the co
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Grad-CAM: Why did you say that? · Issue #66 · arXivTimes/arXivTimes
