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)
Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
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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A simple simple version of tensorboard implemented by d3.js
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
Fitsbook React WebApp. Tool for generating real-time machine learning training statistics and storing model histories. Direct integration with Keras.
FitsBook Python Library. Tool for generating real-time machine learning training statistics and storing model histories. Direct integration with Keras Framework.
Visualizer for deep learning and machine learning models
Neural network visualization toolkit for keras
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