Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Overview

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

This repository contains the code release for:

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding.
David Klindt*, Lukas Schott*, Yash Sharma*, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge†, Dylan Paiton† https://arxiv.org/abs/2007.10930

An example latent traversal using our learned model:
Sample traversal

Abstract: We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that representations can be disentangled if all but a few factors in the environment stay constant at any point in time. As a result, algorithms proposed for this problem have only been tested on carefully constructed datasets with this exact property, leaving it unclear whether they will transfer to natural scenes. Here we provide evidence that objects in segmented natural movies undergo transitions that are typically small in magnitude with occasional large jumps, which is characteristic of a temporally sparse distribution. We leverage this finding and present SlowVAE, a model for unsupervised representation learning that uses a sparse prior on temporally adjacent observations to disentangle generative factors without any assumptions on the number of changing factors. We provide a proof of identifiability and show that the model reliably learns disentangled representations on several established benchmark datasets, often surpassing the current state-of-the-art. We additionally demonstrate transferability towards video datasets with natural dynamics, Natural Sprites and KITTI Masks, which we contribute as benchmarks for guiding disentanglement research towards more natural data domains.

Cite

If you make use of this code in your own work, please cite our paper:

@inproceedings{klindt2021towards,
title={Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding},
author={David A. Klindt and Lukas Schott and Yash Sharma and Ivan Ustyuzhaninov and Wieland Brendel and Matthias Bethge and Dylan Paiton},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=EbIDjBynYJ8}
}

Datasets

Our work also contributes two new datasets.
The Natural Sprites dataset can be downloaded here: https://zenodo.org/record/3948069
The KITTI Masks dataset can be downloaded here: https://zenodo.org/record/3931823

Acknowledgements

The repository is based on the following Beta-VAE reproduction. The MCC metric was adopted from the Time-Contrastive Learning release.

Contact

Owner
Bethge Lab
Perceiving Neural Networks
Bethge Lab
构建一个多源(公众号、RSS)、干净、个性化的阅读环境

2C 构建一个多源(公众号、RSS)、干净、个性化的阅读环境 作为一名微信公众号的重度用户,公众号一直被我设为汲取知识的地方。随着使用程度的增加,相信大家或多或少会有一个比较头疼的问题——广告问题。 假设你关注的公众号有十来个,若一个公众号两周接一次广告,理论上你会面临二十多次广告,实际上会更多,运

howie.hu 678 Dec 28, 2022
This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"

GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If

1.1k Dec 27, 2022
Machine learning classifiers to predict American Sign Language .

ASL-Classifiers American Sign Language (ASL) is a natural language that serves as the predominant sign language of Deaf communities in the United Stat

Tarek idrees 0 Feb 08, 2022
Implementation of Natural Language Code Search in the project CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

CodeBERT-Implementation In this repo we have replicated the paper CodeBERT: A Pre-Trained Model for Programming and Natural Languages. We are interest

Tanuj Sur 4 Jul 01, 2022
CLIPfa: Connecting Farsi Text and Images

CLIPfa: Connecting Farsi Text and Images OpenAI released the paper Learning Transferable Visual Models From Natural Language Supervision in which they

Sajjad Ayoubi 66 Dec 14, 2022
This library is testing the ethics of language models by using natural adversarial texts.

prompt2slip This library is testing the ethics of language models by using natural adversarial texts. This tool allows for short and simple code and v

9 Dec 28, 2021
A list of NLP(Natural Language Processing) tutorials

NLP Tutorial A list of NLP(Natural Language Processing) tutorials built on PyTorch. Table of Contents A step-by-step tutorial on how to implement and

Allen Lee 1.3k Dec 25, 2022
An example project using OpenPrompt under pytorch-lightning for prompt-based SST2 sentiment analysis model

pl_prompt_sst An example project using OpenPrompt under the framework of pytorch-lightning for a training prompt-based text classification model on SS

Zhiling Zhang 5 Oct 21, 2022
Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

Maha 490 Dec 15, 2022
Course project of [email protected]

NaiveMT Prepare Clone this repository git clone [email protected]:Poeroz/NaiveMT.git

Poeroz 2 Apr 24, 2022
Code for lyric-section-to-comment generation based on huggingface transformers.

CommentGeneration Code for lyric-section-to-comment generation based on huggingface transformers. Migrate Guyu model and code (both 12-layers and 24-l

Yawei Sun 8 Sep 04, 2021
Intent parsing and slot filling in PyTorch with seq2seq + attention

PyTorch Seq2Seq Intent Parsing Reframing intent parsing as a human - machine translation task. Work in progress successor to torch-seq2seq-intent-pars

Sean Robertson 159 Apr 04, 2022
Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP)

Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (

jawahar 20 Apr 30, 2022
Built for cleaning purposes in military institutions

Ferramenta do AL Construído para fins de limpeza em instituições militares. Instalação Requer python = 3.2 pip install -r requirements.txt Usagem Exe

0 Aug 13, 2022
OpenAI CLIP text encoders for multiple languages!

Multilingual-CLIP OpenAI CLIP text encoders for any language Colab Notebook · Pre-trained Models · Report Bug Overview OpenAI recently released the pa

Fredrik Carlsson 481 Dec 30, 2022
Checking spelling of form elements

Checking spelling of form elements. You can check the source files of external workflows/reports and configuration files

СКБ Контур (команда 1с) 15 Sep 12, 2022
Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

Multilabel time series classification with LSTM Tensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Re

Aaqib 552 Nov 28, 2022
Code for paper Multitask-Finetuning of Zero-shot Vision-Language Models

Code for paper Multitask-Finetuning of Zero-shot Vision-Language Models

Zhenhailong Wang 2 Jul 15, 2022
Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables

Mortgage-Application-Analysis Create a machine learning model which will predict if the mortgage will be approved or not based on 5 variables: age, in

1 Jan 29, 2022
TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech

TFPNER TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech Named entity recognition (NER), which aims at identifyin

1 Feb 07, 2022