hierarchical-dnn-interpretations
Neural Explanation Tools
Provides an implementation of Hierarchical explanations for Neural Network predictions
Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠(ICLR 2019)
125 stars
9 watching
22 forks
Language: Jupyter Notebook
last commit: about 3 years ago acdaiartificial-intelligenceconvolutional-neural-networksdata-sciencedeep-learningdeep-neural-networksexplainabilityexplainable-aifeature-importanceiclrinterpretabilityinterpretationjupyter-notebookmachine-learningmlneural-networkpythonpytorchstatistics
Related projects:
Repository | Description | Stars |
---|---|---|
understandable-machine-intelligence-lab/quantus | An eXplainable AI toolkit for evaluating and interpreting neural network explanations in various deep learning frameworks. | 556 |
dianna-ai/dianna | A Python package providing an explainable AI interface to research projects | 48 |
namisan/mt-dnn | A PyTorch package implementing multi-task deep neural networks for natural language understanding | 2,238 |
tensorflow/tcav | An interpretability method that provides explanations for neural network predictions by highlighting high-level concepts relevant to classification tasks. | 632 |
deel-ai/xplique | An Explainable AI toolbox that provides various methods and tools to understand and interpret the behavior of neural networks | 644 |
hyeongseokson1/cnn_deconvolution | Improves deconvolution performance using a Convolutional Neural Network | 22 |
ahmedfgad/numpycnn | Builds convolutional neural networks from scratch using NumPy | 572 |
doonny/pipecnn | A tool for accelerating convolutional neural networks on Field-Programmable Gate Arrays (FPGAs) using OpenCL-based hardware design | 1,253 |
marcoancona/deepexplain | A framework for understanding how deep neural networks process input data to produce output | 734 |
100/cranium | A lightweight, portable C implementation of a feedforward artificial neural network library | 592 |
microsoft/archai | Automates the search for optimal neural network configurations in deep learning applications | 467 |
nv-tlabs/gscnn | This code implements a neural network architecture designed to perform semantic segmentation in computer vision tasks. | 920 |
datamllab/xdeep | Provides tools for interpreting deep neural networks | 42 |
hagaygarty/mdcnn | A 3D convolutional neural network framework supporting volumetric inputs and various features like dropout and batch normalization. | 52 |
conan7882/googlenet-inception | An implementation of a deep neural network architecture for image classification using pre-trained models and fine-tuning on the CIFAR-10 dataset. | 282 |