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.

This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks

LMMNN Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks This is the working dire

Giora Simchoni 10 Nov 02, 2022
[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.

[CVPR2022] Thin-Plate Spline Motion Model for Image Animation Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"

yoyo-nb 1.4k Dec 30, 2022
Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On

UPMT Generate fine-tuning samples & Fine-tuning the model & Generate samples by transferring Note On See main.py as an example: from model import PopM

7 Sep 01, 2022
Code for ICCV 2021 paper Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes using Scene Graphs

Graph-to-3D This is the official implementation of the paper Graph-to-3d: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs | arx

Helisa Dhamo 33 Jan 06, 2023
How to use TensorLayer

How to use TensorLayer While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLay

zhangrui 349 Dec 07, 2022
Official implementation of Deep Burst Super-Resolution

Deep-Burst-SR Official implementation of Deep Burst Super-Resolution Publication: Deep Burst Super-Resolution. Goutam Bhat, Martin Danelljan, Luc Van

Goutam Bhat 113 Dec 19, 2022
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks

Introduction This repository includes the source code for "Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks", which is pu

machen 11 Nov 27, 2022
PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"

Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape

Mitch Hill 32 Nov 25, 2022
This repository contains code used to audit the stability of personality predictions made by two algorithmic hiring systems

Stability Audit This repository contains code used to audit the stability of personality predictions made by two algorithmic hiring systems, Humantic

Data, Responsibly 4 Oct 27, 2022
Implementation of DropLoss for Long-Tail Instance Segmentation in Pytorch

[AAAI 2021]DropLoss for Long-Tail Instance Segmentation [AAAI 2021] DropLoss for Long-Tail Instance Segmentation Ting-I Hsieh*, Esther Robb*, Hwann-Tz

Tim 37 Dec 02, 2022
A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

Built with ❤️ by Sam Showalter Contents Overview Installation Dependencies Usage Scripts Standard Execution Environment Development Environment Benchm

SRI-AIC 1 Nov 18, 2021
ATAC: Adversarially Trained Actor Critic

ATAC: Adversarially Trained Actor Critic Adversarially Trained Actor Critic for Offline Reinforcement Learning by Ching-An Cheng*, Tengyang Xie*, Nan

Microsoft 41 Dec 08, 2022
Neural Scene Graphs for Dynamic Scene (CVPR 2021)

Implementation of Neural Scene Graphs, that optimizes multiple radiance fields to represent different objects and a static scene background. Learned representations can be rendered with novel object

151 Dec 26, 2022
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

TorchCAM: class activation explorer Simple way to leverage the class-specific activation of convolutional layers in PyTorch. Quick Tour Setting your C

F-G Fernandez 1.2k Dec 29, 2022
Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

zshicode 1 Nov 18, 2021
Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Sidd Karamcheti 50 Nov 16, 2022
A program to recognize fruits on pictures or videos using yolov5

Yolov5 Fruits Detector Requirements Either Linux or Windows. We recommend Linux for better performance. Python 3.6+ and PyTorch 1.7+. Installation To

Fateme Zamanian 30 Jan 06, 2023
Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

BiDR Repo for WWW 2022 paper: Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval. Requirements torch==

Microsoft 11 Oct 20, 2022
PyTorch implementation of 'Gen-LaneNet: a generalized and scalable approach for 3D lane detection'

(pytorch) Gen-LaneNet: a generalized and scalable approach for 3D lane detection Introduction This is a pytorch implementation of Gen-LaneNet, which p

Yuliang Guo 233 Jan 06, 2023