Unsupervised Abstract Reasoning for Raven’s Problem Matrices

This code is the implementation of our TIP paper.

This is the first unsupervised abstract reasoning method on Raven's Progressive Matrices, it is an extention of our arxiv preprint.

Comparision with some supervised methods.

Average testing accuracy on the RAVEN, I-RAVEN, and PGM dataset

Method	Raven	I-RAVEN	PGM
CNN	36.97	13.26	33.00
ResNet50	86.26	-	42.00
DCNet (ICLR2021)	93.58	49.36	68.57
NCD (Ours)	36.99	48.22	47.62

Generalization test results on PGM dataset

Method	neutral	interpolation	extrapolation
WReN (ICML2018)	62.6	64.4	17.2
DCNet (ICLR2021)	68.6	59.7	17.8
MXGNet (ICLR2020)	89.6	84.6	18.4
NCD (Ours)	47.6	47.0	24.9

Citation

If our code is useful for your research, please cite the following papers.

@article{zhuo2021unsup,
  title={Unsupervised Abstract Reasoning for Raven’s Problem Matrices},
  author={Tao Zhuo, Qiang Huang, and Mohan Kankanhalli},
  journal={IEEE Transactions on Image Processing},
  year={2021}
}

@article{zhuo2020solving,
  title={Solving Raven's Progressive Matrices with Neural Networks},
  author={Tao Zhuo and Mohan Kankanhalli},
  journal={arXiv preprint arXiv:2002.01646},
  year={2020}
}

@inproceedings{iclr2021,  
    author={Tao Zhuo and Mohan Kankanhalli},  
    title={Effective Abstract Reasoning with Dual-Contrast Network},  
    booktitle={International Conference on Learning Representations (ICLR)},      
    year={2021}
}

Unsupervised Abstract Reasoning for Raven’s Problem Matrices

Related tags

Overview

Unsupervised Abstract Reasoning for Raven’s Problem Matrices

Comparision with some supervised methods.

Average testing accuracy on the RAVEN, I-RAVEN, and PGM dataset

Generalization test results on PGM dataset

Citation

Owner

Tao Zhuo

Rich Prosody Diversity Modelling with Phone-level Mixture Density Network

QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries

A collection of models for image - text generation in ACM MM 2021.

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

Sorce code and datasets for "K-BERT: Enabling Language Representation with Knowledge Graph",

基于GRU网络的句子判断程序/A program based on GRU network for judging sentences

Simple Speech to Text, Text to Speech

Telegram AI chat bot written in Python using Pyrogram

An example project using OpenPrompt under pytorch-lightning for prompt-based SST2 sentiment analysis model

Model for recasing and repunctuating ASR transcripts

💫 Industrial-strength Natural Language Processing (NLP) in Python

Sequence modeling benchmarks and temporal convolutional networks

pyupbit 라이브러리를 활용하여 upbit에서 비트코인을 자동매매하는 코드입니다. 조코딩 유튜브 채널에서 자세한 강의 영상을 보실 수 있습니다.

Non-Autoregressive Predictive Coding

NLP-Project - Used an API to scrape 2000 reddit posts, then used NLP analysis and created a classification model to mixed succcess

🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Repository of the Code to Chatbots, developed in Python

문장단위로 분절된 나무위키 데이터셋. Releases에서 다운로드 받거나, tfds-korean을 통해 다운로드 받으세요.

a chinese segment base on crf

This repository details the steps in creating a Part of Speech tagger using Trigram Hidden Markov Models and the Viterbi Algorithm without using external libraries.