Keras implementation of class activation mapping
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Updated
Aug 5, 2017 - Python
Keras implementation of class activation mapping
Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)
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Attribution (or visual explanation) methods for understanding video classification networks. Demo codes for WACV2021 paper: Towards Visually Explaining Video Understanding Networks with Perturbation.
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MNIST classifier with a graphical user interface and a canvas for drawing the digits, doing classifying in real time
Keras implementation of class activation mapping
A simple simple version of tensorboard implemented by d3.js
Fitsbook React WebApp. Tool for generating real-time machine learning training statistics and storing model histories. Direct integration with Keras.
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