Unsupervised Abstract Reasoning for Raven’s Problem Matrices

Related tags

Text Data & NLP ncd
Overview

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}
}
You might also like...
source code for paper: WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach.

WhiteningBERT Source code and data for paper WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach. Preparation git clone https://github.com

[EMNLP 2021] LM-Critic: Language Models for Unsupervised Grammatical Error Correction
[EMNLP 2021] LM-Critic: Language Models for Unsupervised Grammatical Error Correction

LM-Critic: Language Models for Unsupervised Grammatical Error Correction This repo provides the source code & data of our paper: LM-Critic: Language M

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge Correlation Explanation (CorEx) is a topic model that yields rich topics tha

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.
A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

A Non-Autoregressive Transformer based TTS, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS.

Rhythm-Finder is a unsupervised ML driven python powered web-application that can find the songs that suits you.
Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation

Unsupervised Document Expansion for Information Retrieval with Stochastic Text Generation Official Code Repository for the paper "Unsupervised Documen

Unsupervised intent recognition

INTENT author: steeve LAQUITAINE description: deployment pattern: currently batch only Setup & run git clone https://github.com/slq0/intent.git bash

This project uses unsupervised machine learning to identify correlations between daily inoculation rates in the USA and twitter sentiment in regards to COVID-19.
This project uses unsupervised machine learning to identify correlations between daily inoculation rates in the USA and twitter sentiment in regards to COVID-19.

Twitter COVID-19 Sentiment Analysis Members: Christopher Bach | Khalid Hamid Fallous | Jay Hirpara | Jing Tang | Graham Thomas | David Wetherhold Pro

Implementaion of our ACL 2022 paper Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation

Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation This is the implementaion of our paper: Bridging the

The FinQA dataset from paper: FinQA: A Dataset of Numerical Reasoning over Financial Data

Data and code for EMNLP 2021 paper "FinQA: A Dataset of Numerical Reasoning over Financial Data"

Zhiyu Chen 114 Dec 29, 2022
Unsupervised text tokenizer for Neural Network-based text generation.

SentencePiece SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabu

Google 6.4k Jan 1, 2023
Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)

VK.com 847 Dec 19, 2022
Unsupervised text tokenizer for Neural Network-based text generation.

SentencePiece SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabu

Google 4.8k Feb 18, 2021
Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)

VK.com 718 Feb 18, 2021
One Stop Anomaly Shop: Anomaly detection using two-phase approach: (a) pre-labeling using statistics, Natural Language Processing and static rules; (b) anomaly scoring using supervised and unsupervised machine learning.

One Stop Anomaly Shop (OSAS) Quick start guide Step 1: Get/build the docker image Option 1: Use precompiled image (might not reflect latest changes):

Adobe, Inc. 148 Dec 26, 2022
Unsupervised Language Modeling at scale for robust sentiment classification

** DEPRECATED ** This repo has been deprecated. Please visit Megatron-LM for our up to date Large-scale unsupervised pretraining and finetuning code.

NVIDIA Corporation 1k Nov 17, 2022
A library for Multilingual Unsupervised or Supervised word Embeddings

MUSE: Multilingual Unsupervised and Supervised Embeddings MUSE is a Python library for multilingual word embeddings, whose goal is to provide the comm

Facebook Research 3k Jan 6, 2023
Phrase-Based & Neural Unsupervised Machine Translation

Unsupervised Machine Translation This repository contains the original implementation of the unsupervised PBSMT and NMT models presented in Phrase-Bas

Facebook Research 1.5k Dec 28, 2022