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.

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting

Guided Internet-delivered Cognitive Behavioral Therapy Adherence Forecasting #Dataset The folder "Dataset" contains the dataset use in this work and m

0 Jan 08, 2022
Morphable Detector for Object Detection on Demand

Morphable Detector for Object Detection on Demand (ICCV 2021) PyTorch implementation of the paper Morphable Detector for Object Detection on Demand. I

9 Feb 23, 2022
Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation

Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation The code of: Context Decoupling Augmentation for Weakly Supervised Semanti

54 Dec 12, 2022
Parris, the automated infrastructure setup tool for machine learning algorithms.

README Parris, the automated infrastructure setup tool for machine learning algorithms. What Is This Tool? Parris is a tool for automating the trainin

Joseph Greene 319 Aug 02, 2022
This is a simple framework to make object detection dataset very quickly

FastAnnotation Table of contents General info Requirements Setup General info This is a simple framework to make object detection dataset very quickly

Serena Tetart 1 Jan 24, 2022
This repository contains a set of codes to run (i.e., train, perform inference with, evaluate) a diarization method called EEND-vector-clustering.

EEND-vector clustering The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates

45 Dec 26, 2022
TFOD-MASKRCNN - Tensorflow MaskRCNN With Python

Tensorflow- MaskRCNN Steps git clone https://github.com/amalaj7/TFOD-MASKRCNN.gi

Amal Ajay 2 Jan 18, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
JittorVis - Visual understanding of deep learning models

JittorVis: Visual understanding of deep learning model JittorVis is an open-source library for understanding the inner workings of Jittor models by vi

thu-vis 182 Jan 06, 2023
Code release for ConvNeXt model

A ConvNet for the 2020s Official PyTorch implementation of ConvNeXt, from the following paper: A ConvNet for the 2020s. arXiv 2022. Zhuang Liu, Hanzi

Meta Research 4.6k Jan 08, 2023
An executor that performs image segmentation on fashion items

ClothingSegmenter U2NET fashion image/clothing segmenter based on https://github.com/levindabhi/cloth-segmentation Overview The ClothingSegmenter exec

Jina AI 5 Mar 30, 2022
(under submission) Bayesian Integration of a Generative Prior for Image Restoration

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration Authors: Majed El Helou, and Sabine Süsstrunk {Note: p

Majed El Helou 22 Dec 17, 2022
Plotting points that lie on the intersection of the given curves using gradient descent.

Plotting intersection of curves using gradient descent Webapp Link --- What's the app about Why this app Plotting functions and their intersection. A

Divakar Verma 2 Jan 09, 2022
Tutorial: Introduction to Graph Machine Learning, with Jupyter notebooks

GraphMLTutorialNLDL22 Tutorial NLDL22: Introduction to Graph Machine Learning, with Jupyter notebooks This tutorial takes place during the conference

UiT Machine Learning Group 3 Jan 10, 2022
A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal

A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases,

Chris Hughes 110 Dec 23, 2022
How Effective is Incongruity? Implications for Code-mix Sarcasm Detection.

Code for the paper: How Effective is Incongruity? Implications for Code-mix Sarcasm Detection - ICON ACL 2021

2 Jun 05, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Near-Duplicate Video Retrieval with Deep Metric Learning This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retr

Liming Jiang 238 Nov 25, 2022
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

Hila Chefer 489 Jan 07, 2023
Code and datasets for the paper "Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction" (RA-L, 2021)

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction This is the code for the paper Combining E

Robotics and Perception Group 69 Dec 26, 2022
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging object detection dataset

CPPE - 5 CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization

Rishit Dagli 53 Dec 17, 2022