Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

Related tags

Deep Learningauto
Overview

pre-commit.ci status

Project 3 - FYS-STK4155

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.


The folder p3 contains the source code for our python package where the project required implementations are defined.

To format the code there is added a pre-commit configuration so the code follows a common standard between the members of the group. After pre-commit is installed in virtual environment or globally, activate it for this repository by running pre-commit install. It will now run configured linters and formatters each time you make a commit.

Setup using virtual environment

cd 
   
# Create a virtual environment
python -m venv venv
# Activate it
venv\Scripts\activate.bat # or on linux/mac: . venv/bin/activate
# Install the package and dependencies as an editable package in the venv
pip install -e .[dev,testing]

If you are using conda something like this should maybe work:

cd 
   
conda create --prefix ./env
conda activate ./env
pip install -e .[dev,testing]
# or maybe
conda install conda-build
conda develop . -n 
   

Running tests and check coverage

To run the tests in tests folder we use pytest and coverage, who is installed, if set-up is done as described above.

# to run tests:
(.venv)$ pytest
# to run coverage
(.venv)$ coverage run -m pytest && coverage report -m

Training

❯ python -m p3 --help
usage: __main__.py [-h] [--dataset DATASET] [--epochs EPOCHS]
                   [--batch-size BATCH_SIZE] [--lr LR]

optional arguments:
  -h, --help            show this help message and exit
  --dataset DATASET     path to dataset root. If dataset is not found at
                        location it will be downloaded to this location.
                        Default: './dataset'
  --epochs EPOCHS       train for number of epochs
  --batch-size BATCH_SIZE
                        number of samples in a batch
  --lr LR               step size during optimization
Owner
Tom-R.T.Kvalvaag
Tom-R.T.Kvalvaag
Latent Execution for Neural Program Synthesis

Latent Execution for Neural Program Synthesis This repo provides the code to replicate the experiments in the paper Xinyun Chen, Dawn Song, Yuandong T

Xinyun Chen 16 Oct 02, 2022
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

Phil Wang 209 Dec 28, 2022
Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021.

Code of the paper "Deep Human Dynamics Prior" in ACM MM 2021. Figure 1: In the process of motion capture (mocap), some joints or even the whole human

Shinny cui 3 Oct 31, 2022
Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization".

SAPE Project page Paper Official implementation for the paper "SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization". Environment Cre

36 Dec 09, 2022
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"

Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng Internati

Princeton Vision & Learning Lab 115 Jan 04, 2023
Setup freqtrade/freqUI on Heroku

UNMAINTAINED - REPO MOVED TO https://github.com/p-zombie/freqtrade Creating the app git clone https://github.com/joaorafaelm/freqtrade.git && cd freqt

João 51 Aug 29, 2022
The code release of paper Low-Light Image Enhancement with Normalizing Flow

[AAAI 2022] Low-Light Image Enhancement with Normalizing Flow Paper | Project Page Low-Light Image Enhancement with Normalizing Flow Yufei Wang, Renji

Yufei Wang 176 Jan 06, 2023
Original Implementation of Prompt Tuning from Lester, et al, 2021

Prompt Tuning This is the code to reproduce the experiments from the EMNLP 2021 paper "The Power of Scale for Parameter-Efficient Prompt Tuning" (Lest

Google Research 282 Dec 28, 2022
A MNIST-like fashion product database. Benchmark

Fashion-MNIST Table of Contents Why we made Fashion-MNIST Get the Data Usage Benchmark Visualization Contributing Contact Citing Fashion-MNIST License

Zalando Research 10.5k Jan 08, 2023
An excellent hash algorithm combining classical sponge structure and RNN.

SHA-RNN Recurrent Neural Network with Chaotic System for Hash Functions Anonymous Authors [摘要] 在这次作业中我们提出了一种新的 Hash Function —— SHA-RNN。其以海绵结构为基础,融合了混

Houde Qian 5 May 15, 2022
From this paper "SESNet: A Semantically Enhanced Siamese Network for Remote Sensing Change Detection"

SESNet for remote sensing image change detection It is the implementation of the paper: "SESNet: A Semantically Enhanced Siamese Network for Remote Se

1 May 24, 2022
Semi-supervised Implicit Scene Completion from Sparse LiDAR

Semi-supervised Implicit Scene Completion from Sparse LiDAR Paper Created by Pengfei Li, Yongliang Shi, Tianyu Liu, Hao Zhao, Guyue Zhou and YA-QIN ZH

114 Nov 30, 2022
Aydin is a user-friendly, feature-rich, and fast image denoising tool

Aydin is a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms.

Royer Lab 99 Dec 14, 2022
This repository contains the code for the paper in EMNLP 2021: "HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression".

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression This repository contains the code for the paper in EM

Chenhe Dong 2 Mar 24, 2022
E-Ink Magic Calendar that automatically syncs to Google Calendar and runs off a battery powered Raspberry Pi Zero

MagInkCal This repo contains the code needed to drive an E-Ink Magic Calendar that uses a battery powered (PiSugar2) Raspberry Pi Zero WH to retrieve

2.8k Dec 28, 2022
Edge-aware Guidance Fusion Network for RGB-Thermal Scene Parsing

EGFNet Edge-aware Guidance Fusion Network for RGB-Thermal Scene Parsing Dataset and Results Test maps: 百度网盘 提取码:zust Citation @ARTICLE{ author={Zhou,

ShaohuaDong 10 Dec 08, 2022
Python library for science observations from the James Webb Space Telescope

JWST Calibration Pipeline JWST requires Python 3.7 or above and a C compiler for dependencies. Linux and MacOS platforms are tested and supported. Win

Space Telescope Science Institute 386 Dec 30, 2022
Official PyTorch implementation of RIO

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection Figure 1: Our proposed Resampling at image-level and obect-

NVIDIA Research Projects 17 May 20, 2022
Train CPPNs as a Generative Model, using Generative Adversarial Networks and Variational Autoencoder techniques to produce high resolution images.

cppn-gan-vae tensorflow Train Compositional Pattern Producing Network as a Generative Model, using Generative Adversarial Networks and Variational Aut

hardmaru 343 Dec 29, 2022