Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

Related tags

Deep LearningVOTEN
Overview

Configurations

  • Change HOME_PATH in CONFIG.py as the current path

Data Prepare

CENSINCOME

  • Download data
  • Put census-income.data and census-income.test in Data/Downloads/census-income
  • Run DataProcess/CENSINCOME/process.py

COVTYPE

  • Download data
  • Put covtype.data in Data/Downloads/COVTYPE/
  • Run DataProcess/COVTYPE/process.py

IJCAI18X

  • Download data
  • Generate train.csv and test.csv with xgboost1.py
  • Put train.csv and test.cvs in Data/RawData/IJCAI18X
  • Run DataProcess/IJCAI18X/process.py

KDDCUP19

  • Download data
  • Put train_queries.csv, train_plans.csv, train_clicks.csv, profiles.csv in Data/Downloads/KDDCUP19P1
  • Run DataProcess/KDC/process.py

Training

Train and save the model

  • Use the scripts in Train/{DATASET}/
  • Will train the model and save the model in Data/Saved/{DATASET}/[VOTERS or DNN]/model

Explanation

Generate intermediate results of voting analysis

  • Run SaveInter/save.py

Visualizations for explanation

  • Run E1 to E6 in Explain/analysis, the visualizations will be saved in Explain/out

Demo system for local/global decision path visualization

  • Open the path of the django project in Site/
  • Start the server: python manage.py runserver 0.0.0.0:8000
  • Use the system in browser with URL: 127.0.0.1:8000/{global/local}/{covtyp/kdc/ijcai/cens}

Citation

If you use this code, please cite our paper "Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation" published in NeurIPS 2021.

Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Code for KSDAgg: a KSD aggregated goodness-of-fit test This GitHub repository contains the code for the reproducible experiments presented in our pape

Antonin Schrab 5 Dec 15, 2022
Training vision models with full-batch gradient descent and regularization

Stochastic Training is Not Necessary for Generalization -- Training competitive vision models without stochasticity This repository implements trainin

Jonas Geiping 32 Jan 06, 2023
《Towards High Fidelity Face Relighting with Realistic Shadows》(CVPR 2021)

Towards High Fidelity Face-Relighting with Realistic Shadows Andrew Hou, Ze Zhang, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2021. T

114 Dec 10, 2022
TensorFlow implementation for Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How TensorFlow implementation for Bayesian Modeling and Unce

Shen Lab at Texas A&M University 8 Sep 02, 2022
PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluation of Visual Stories via Semantic Consistency"

Improving Generation and Evaluation of Visual Stories via Semantic Consistency PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluat

Adyasha Maharana 28 Dec 08, 2022
Transformers are Graph Neural Networks!

🚀 Gated Graph Transformers Gated Graph Transformers for graph-level property prediction, i.e. graph classification and regression. Associated article

Chaitanya Joshi 46 Jun 30, 2022
Source code for Task-Aware Variational Adversarial Active Learning

Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u

27 Nov 23, 2022
TalkingHead-1KH is a talking-head dataset consisting of YouTube videos

TalkingHead-1KH Dataset TalkingHead-1KH is a talking-head dataset consisting of YouTube videos, originally created as a benchmark for face-vid2vid: On

173 Dec 29, 2022
This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset

HiRID-ICU-Benchmark This repository contains the needed resources to build the HIRID-ICU-Benchmark dataset for which the manuscript can be found here.

Biomedical Informatics at ETH Zurich 30 Dec 16, 2022
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting This repository is the official implementation of Spectral Temporal Gr

Microsoft 306 Dec 29, 2022
Predicting Tweet Sentiment Maching Learning and streamlit

Predicting-Tweet-Sentiment-Maching-Learning-and-streamlit (I prefere using Visual Studio Code ) Open the folder in VS Code Run the first cell in requi

1 Nov 20, 2021
Active window border replacement for window managers.

xborder Active window border replacement for window managers. Usage git clone https://github.com/deter0/xborder cd xborder chmod +x xborders ./xborder

deter 250 Dec 30, 2022
Real-time Object Detection for Streaming Perception, CVPR 2022

StreamYOLO Real-time Object Detection for Streaming Perception Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Sun Jian Real-time Object Detection

Jinrong Yang 237 Dec 27, 2022
A python comtrade load library accelerated by go

Comtrade-GRPC Code for python used is mainly from dparrini/python-comtrade. Just patch the code in BinaryDatReader.parse for parsing a little more eff

Bo 1 Dec 27, 2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems

Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems This repository is the official implementation of Rever

6 Aug 25, 2022
A treasure chest for visual recognition powered by PaddlePaddle

简体中文 | English PaddleClas 简介 飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集,助力使用者训练出更好的视觉模型和应用落地。 近期更新 2021.11.1 发布PP-ShiTu技术报告,新增饮料识别demo 2021.10.23 发

4.6k Dec 31, 2022
Official code for article "Expression is enough: Improving traffic signal control with advanced traffic state representation"

1 Introduction Official code for article "Expression is enough: Improving traffic signal control with advanced traffic state representation". The code s

Liang Zhang 10 Dec 10, 2022
Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs)

Why Spectral Normalization Stabilizes GANs: Analysis and Improvements [paper (NeurIPS 2021)] [paper (arXiv)] [code] Authors: Zinan Lin, Vyas Sekar, Gi

Zinan Lin 32 Dec 16, 2022
TensorFlow implementation of "Variational Inference with Normalizing Flows"

[TensorFlow 2] Variational Inference with Normalizing Flows TensorFlow implementation of "Variational Inference with Normalizing Flows" [1] Concept Co

YeongHyeon Park 7 Jun 08, 2022
Voice Gender Recognition

In this project it was used some different Machine Learning models to identify the gender of a voice (Female or Male) based on some specific speech and voice attributes.

Anne Livia 1 Jan 27, 2022