duralava is a neural network which can simulate a lava lamp in an infinite loop.

Related tags

Deep Learningduralava
Overview

duralava

duralava is a neural network which can simulate a lava lamp in an infinite loop.

Example

This is not a real lava lamp but a "fake" one generated by duralava.

duralava neural network deep learning lava lamp

Novelty

duralava can

  • learn a physical process (a lava lamp).
  • generate an arbitarily long sequence of output, without diverging even after hours (outputting tens of thousands of frames).

How it works

Generative Adversarial Networks (GANs) can learn to generate new samples of data. For example, a GAN can be trained to output images of a lava lamp which look as real as possible. To accomplish this, the GAN gets an input vector with normally distributed noise. For duralava this vector is of length 64. Based on this random noise vector it generates a lava lamp image. The random vector thus encodes the state of the lava lamp.

For training, the GAN is presented a real image of a lava lamp and also one of the fake lava lamp and then it learns to make the fake ones look as real as possible.

For a lava lamp, a sequence of images has to be created. This sequence should in fact be infinite since a lava lamp can run forever. Thus the GAN should learn to output an arbitrarily long sequence of lava lamp images as a video. This is achieved by using a recurrent neural network (RNN). The RNN gets the 64 element noise vector of time step t and outputs the 64 element noise vector for time stemp t+1.

The tricky part is to make sure that the state of the lava lamp (the 64 element random noise vector) remains stable. It could for example happen that over time the distribution of noise in the vector diverges from a normal distribution the mean becomes 10 and the standard deviation 52. In this case, the output images of the lava lamps wouldn't be correct anymore as the GAN was trained to expect the input vector to be normally distributed. To solve this problem, I make sure that in training the output of the RNN stays normally distributed. This is accomplished by adding penalization terms in the training which discourage the noise to diverge from the normal distribution.

Low-hanging fruit

I trained on a MacBook Air with an M1 SoC with 16 GB of shared memory for CPU and GPU. Thus, memory was the limiting factor in my experiments.

With more memory, one could

  • Increase the resolution (currently 64x64 pixels)
  • Increase the training sequence length (currently 20)
  • Increase the batch size (currently 32)
Owner
Maximilian Bachl
Maximilian Bachl
Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

Emirhan BULUT 28 Dec 04, 2021
Workshop Materials Delivered on 28/02/2022

intro-to-cnn-p1 Repo for hosting workshop materials delivered on 28/02/2022 Questions you will answer in this workshop Learning Objectives What are co

Beginners Machine Learning 5 Feb 28, 2022
Project for music generation system based on object tracking and CGAN

Project for music generation system based on object tracking and CGAN The project was inspired by MIDINet: A Convolutional Generative Adversarial Netw

1 Nov 21, 2021
这是一个利用facenet和retinaface实现人脸识别的库,可以进行在线的人脸识别。

Facenet+Retinaface:人脸识别模型在Pytorch当中的实现 目录 注意事项 Attention 所需环境 Environment 文件下载 Download 预测步骤 How2predict 参考资料 Reference 注意事项 该库中包含了两个网络,分别是retinaface和

Bubbliiiing 102 Dec 30, 2022
Code for ECIR'20 paper Diagnosing BERT with Retrieval Heuristics

Bert Axioms This is the repository with the code for the Paper Diagnosing BERT with Retrieval Heuristics Required Data In order to run this code, you

Arthur Câmara 5 Jan 21, 2022
Official code for the ICCV 2021 paper "DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders"

DECA Official code for the ICCV 2021 paper "DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders". All the code is writte

23 Dec 01, 2022
Simple streamlit app to demonstrate HERE Tour Planning

Table of Contents About the Project Built With Getting Started Prerequisites Installation Usage Roadmap Contributing License Acknowledgements About Th

Amol 8 Sep 05, 2022
Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper

AnimeGAN - Deep Convolutional Generative Adverserial Network PyTorch implementation of DCGAN introduced in the paper: Unsupervised Representation Lear

Rohit Kukreja 23 Jul 21, 2022
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning DouZero is a reinforcement learning framework for DouDizhu (斗地主), t

Kwai Inc. 3.1k Jan 04, 2023
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 02, 2023
Code repository for "Free View Synthesis", ECCV 2020.

Free View Synthesis Code repository for "Free View Synthesis", ECCV 2020. Setup Install the following Python packages in your Python environment - num

Intelligent Systems Lab Org 253 Dec 07, 2022
Trajectory Prediction with Graph-based Dual-scale Context Fusion

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion Introduction This is the project page of the paper Lu Zhang, Peiliang Li, Jing C

HKUST Aerial Robotics Group 103 Jan 04, 2023
The description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts.

FMFCC-A This project is the description of FMFCC-A (audio track of FMFCC) dataset and Challenge resluts. The FMFCC-A dataset is shared through BaiduCl

18 Dec 24, 2022
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022
Animal Sound Classification (Cats Vrs Dogs Audio Sentiment Classification)

this is a simple artificial neural network model using deep learning and torch-audio to classify cats and dog sounds.

crispengari 3 Dec 05, 2022
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images This repository contains the implementation of our paper MetaAvatar: Learni

sfwang 96 Dec 13, 2022
A real-time speech emotion recognition application using Scikit-learn and gradio

Speech-Emotion-Recognition-App A real-time speech emotion recognition application using Scikit-learn and gradio. Requirements librosa==0.6.3 numpy sou

Son Tran 6 Oct 04, 2022
General Assembly Capstone: NBA Game Predictor

Project 6: Predicting NBA Games Problem Statement Can I predict the results of NBA games from the back-half of a season from the opening half of the s

Adam Muhammad Klesc 1 Jan 14, 2022
Real time sign language recognition

The proposed work aims at converting american sign language gestures into English that can be understood by everyone in real time.

Mohit Kaushik 6 Jun 13, 2022
Codes for NAACL 2021 Paper "Unsupervised Multi-hop Question Answering by Question Generation"

Unsupervised-Multi-hop-QA This repository contains code and models for the paper: Unsupervised Multi-hop Question Answering by Question Generation (NA

Liangming Pan 70 Nov 27, 2022