GitHub topics: autoencoders

Repositories

cwkx/GON

Gradient Origin Networks - a new type of generative model that is able to quickly learn a latent representation without an encoder

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alexandru-dinu/cae

Compressive AutoEncoder.

Language: Python - Size: 7.25 MB - Last synced at: 5 days ago - Pushed at: 2 months ago - Stars: 175 - Forks: 32

Rohit-Sharma-RS/ML-and-DL

My Machine learning and deep learning projects and templates

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AR-Version2/Intelligent-SAP-Financial-Integrity-Monitor

Intelligent SAP Financial Integrity Monitor (POC): Hybrid AI/ML (IF, LOF, AE) & rules-based anomaly detection on SAP FAGLFLEXA data using Python/Streamlit

Language: Python - Size: 4.05 MB - Last synced at: 4 days ago - Pushed at: 6 days ago - Stars: 0 - Forks: 0

Tanmoy0077/Credit-Card-Fraud-Detection

Credit Card Fraud Detection using Machine Learning and AutoEncoders

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antonin-lfv/ECG_Generator

ECG generator

Language: Python - Size: 10.2 MB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 0 - Forks: 0

ranimeshehata/Face-Recognition

This project implements a face recognition pipeline using the AT&T Face Dataset (ORL Dataset). It includes dimensionality reduction techniques like PCA, clustering algorithms such as K-Means and GMM, and an optional Autoencoder-based feature extraction.

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GabrieleLozupone/LDAE

Official PyTorch implementation of "Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging". LDAE is a novel unsupervised framework for 3D medical imaging that combines a latent diffusion model with semantic controls.

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DrAdrianDC/Portfolio-for-Data-Science

This repository contains a collection of end-to-end machine learning and data science projects I have worked on

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curiousily/Deep-Learning-For-Hackers

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Language: Jupyter Notebook - Size: 22.2 MB - Last synced at: 6 days ago - Pushed at: about 5 years ago - Stars: 1,044 - Forks: 439

paucablop/chemotools

Integrate your chemometric tools with the scikit-learn API 🧪 🤖

Language: Python - Size: 30.4 MB - Last synced at: 8 days ago - Pushed at: 10 days ago - Stars: 53 - Forks: 6

EthanJamesLew/AutoKoopman

AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.

Language: Python - Size: 38 MB - Last synced at: 7 days ago - Pushed at: about 1 year ago - Stars: 73 - Forks: 9

Jaguar1225/DL-for-Plasma-Dynamics

Coding team in Nanoscale Processing Laboratory, SKKU

Language: Python - Size: 9.98 MB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 0 - Forks: 0

jbramburger/DataDrivenDynSyst

Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems

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oscarhoffmann3487/TDDE70_Deep_Learning

This repository contains my solutions for the TDDE70 Deep Learning course (Linköping University, Spring 2024), including an intro notebook and four labs.

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HROlive/Applications-of-AI-for-Anomaly-Detection

Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

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DREI-8/Autoencoder-Denoising-Diffusion

Image denoising and generation using autoencoders and diffusion models.

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CompVis/net2net

Network-to-Network Translation with Conditional Invertible Neural Networks

Language: Python - Size: 75.2 MB - Last synced at: 17 days ago - Pushed at: over 2 years ago - Stars: 226 - Forks: 21

Josuercuevas/own_repos

Ideas developed or integrated with other publicly available projects

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nmichlo/disent

🧶 Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

Language: Python - Size: 18.8 MB - Last synced at: 3 days ago - Pushed at: about 2 years ago - Stars: 128 - Forks: 17

AutoViML/featurewiz

Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.

Language: Python - Size: 10.6 MB - Last synced at: 28 days ago - Pushed at: 3 months ago - Stars: 638 - Forks: 95

serengil/tensorflow-101

TensorFlow 101: Introduction to Deep Learning

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humanlab/WhiSPA

WhiSPA: Whisper Semantically-Psychologically Aligned with Self-Supervised Contrastive Learning

Language: Python - Size: 4.06 MB - Last synced at: 23 days ago - Pushed at: 23 days ago - Stars: 6 - Forks: 0

orelz890/CS231n_Assignments_And_Summary

👁️‍🗨️ Computer Vision Concepts Summary & Assignments 📚🔍

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mohit1106/Fraud-Detection-In-Financial-Transactions

an anomaly detection system on 284,807 transactions, achieving an AUC of ~0.972 with CNNs and Autoencoders.

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petrobras/WPRAutoencoders

This is one of Petrobras' open repositories on GitHub. It contains the WPRAutoencoders project which encompasses a wellbore pressure response generator, a dataset of 20.000 synthetic pressure responses and an autoencoder neural network capable of clustering this data based on transmissibility and reservoir geometry.

Language: Jupyter Notebook - Size: 17 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 44 - Forks: 8

harveyslash/Deep-Steganography

Hiding Images within other images using Deep Learning

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MiaHuebscher/Skin-Cancer-Analysis-and-Detection

applying machine learning tactics (CNNs and anomaly detection) to identify skin cancer from images and patient metadata

Language: Jupyter Notebook - Size: 19.7 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

khanhnamle1994/MetaRec

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models

Language: Python - Size: 626 MB - Last synced at: 12 days ago - Pushed at: over 2 years ago - Stars: 303 - Forks: 77

haoliuhl/language-quantized-autoencoders

Language Quantized AutoEncoders

Language: Python - Size: 37.1 KB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 103 - Forks: 5

xianglin226/Benchmarking-Single-Cell-Perturbation

Single-Cell (Perturbation) Model Library

Size: 213 KB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 42 - Forks: 4

The-AI-Summer/Introduction-to-Deep-Learning-and-Neural-Networks-Course

Code snippets and solutions for the Introduction to Deep Learning and Neural Networks Course hosted in educative.io

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GiulioTaralli/PyTorch_DeepLearning

This repository contains exercises for learning the PyTorch library and Deep Learning models. Some of these topics were covered during the university course "Neural Networks and Deep Learning", while others were explored independently to deepen expertise in the field.

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uni-projects-master/deep-learning-with-pytorch

This repository contains assignments and projects developed during the Deep Learning course.

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Chandrashekar0123/Deep_Learning

This Repository consists of all Deep Learning related projects

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graphixxxx/Denoising_AutoEncoder

This project integrates Autoencoders, PCA, and CNNs for efficient image processing, combining dimensionality reduction, denoising, and enhanced feature extraction for image analysis and compression.

Size: 1.95 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

milaan9/Deep_Learning_Algorithms_from_Scratch

This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON

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aqibsaeed/Place-Recognition-using-Autoencoders-and-NN

Place recognition with WiFi fingerprints using Autoencoders and Neural Networks

Language: Jupyter Notebook - Size: 63.5 KB - Last synced at: about 1 month ago - Pushed at: over 7 years ago - Stars: 265 - Forks: 61

ImKeTT/CTG-latentAEs

[Paperlist] Awesome paper list of controllable text generation via latent auto-encoders. Contributions of any kind are welcome.

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Warvito/Normative-modelling-using-deep-autoencoders

Normative modelling using deep autoencoders: a multi-cohort study on mild cognitive impairment and Alzheimer’s disease

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shadoisper/k-sparse-autoencoder

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villacampaporta/synthetic-dielectric-data-gen

🚀 Synthetic Data Generation for Dielectric Characterization using Machine Learning | TVAE & CTGAN for Data Augmentation in Sensor Applications

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TsLu1s/segmentae

SegmentAE: A Python Library for Anomaly Detection Optimization

Language: Python - Size: 111 KB - Last synced at: 19 days ago - Pushed at: 4 months ago - Stars: 7 - Forks: 1

Vinit-source/Deep-Learning-Tasks

Deep Learning assignments performed using PyTorch during MTech at IIT Jodhpur

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arjunravi26/deep_learning

This repo contains codes and notes to learn deep learning

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shyamsn97/barebones-ai

Machine Learning and Deep Learning algorithms built from scratch

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alexchilton/CAS_AML_Module_3

CAS AML Uni Bern Module 3 covering AutoEncoders, Diffusion Models and some basic tooling

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jeugregg/ocean_protocol_eth_pred

Cryptocurrency AI prediction model

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arjuntheprogrammer/TheSchoolOfAI

Projects for The School of AI

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saurabhdeshpande93/gp-auto-regression

Language: Python - Size: 65.7 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 4 - Forks: 0

kris96tian/MOmics_thesis

COMPARISON OF MULTI-OMICS INTEGRATION METHODS

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AllenInstitute/coupledAE-patchseq

Multimodal data alignment and cell type analysis with coupled autoencoders.

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EPSOFT/Autoencoder

Autoencoder

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greenelab/DAPS

Denoising Autoencoders for Phenotype Stratification

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12danielLL/Neural_Networks_Project

The project focuses on analyzing neural activity data to classify neuron types (spiny and aspiny). It integrates unsupervised learning methods (PCA, Autoencoders) and supervised learning models (Logistic Regression, MLP) to build accurate classifiers that effectively analyze neurons' electrical responses.

Language: Jupyter Notebook - Size: 2.93 MB - Last synced at: about 2 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

BenChaliah/Superposition-Transformer

a novel architecture that leverages Autoencoders to superimpose the hidden representations of a base model and a fine-tuned model within a shared parameter space. Using B-spline-based blending coefficients and autoencoders that adaptively reconstruct the original hidden states based on the input data distribution.

Language: Jupyter Notebook - Size: 7.17 MB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 42 - Forks: 1

Mehrab-Kalantari/Autoencoder-Image-Retrieval

Image retrieval using a simple autoencoder on CIFAR-10 dataset

Language: Jupyter Notebook - Size: 191 KB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

mpatacchiola/Y-AE

Official Tensorflow implementation of the paper "Y-Autoencoders: disentangling latent representations via sequential-encoding", Pattern Recognition Letters (2020)

Language: Python - Size: 13.1 MB - Last synced at: about 1 month ago - Pushed at: over 4 years ago - Stars: 52 - Forks: 9

numaproj/numalogic

Collection of operational time series ML models and tools

Language: Python - Size: 46.3 MB - Last synced at: 1 day ago - Pushed at: 7 months ago - Stars: 168 - Forks: 31

IAmFarrokhnejad/Wine-Quality-Classification

Wine Quality Classification Using Deep Learning

Language: Python - Size: 104 KB - Last synced at: about 1 month ago - Pushed at: 5 months ago - Stars: 1 - Forks: 0

xnought/introduction-to-autoencoders

Visualize autoencoder model training right in your browser. VISxAI 2021

Language: Svelte - Size: 4.74 MB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

rakibhhridoy/AnomalyDetectionInTimeSeriesData-Keras

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

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mrdvince/autoencoders

Autoencoders are neural networks used for data compression, image de-noising, and dimensionality reduction. Using PyTorch.

Language: Python - Size: 338 KB - Last synced at: about 1 month ago - Pushed at: over 4 years ago - Stars: 6 - Forks: 0

greenelab/adage

Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016

Language: Python - Size: 79.1 MB - Last synced at: 5 days ago - Pushed at: almost 9 years ago - Stars: 61 - Forks: 30

AI-Club-SIT-Pune/Melodify

Exploring Generative Music using Autoencoders

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fazelelham32/DL-Workshop-python-matlab-R-programming

Codes and Project for Deep Learning

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wecarsoniv/augmented-pca

Repository for the AugmentedPCA Python package.

Language: Python - Size: 63 MB - Last synced at: 7 days ago - Pushed at: 6 months ago - Stars: 10 - Forks: 0

OmarFaig/VAE_vs_GAN

Developed and experimented with linear, convolutional, and variational autoencoder (VAE) and GAN architectures for image generation, analyzing the performance in terms of reconstruction quality and latent space representation

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samuel-adekunle/Autoencoders

Autoencoders Tutorial

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Nishant2018/AutoEncoder-Generative-AI-MNIST

Autoencoders are a type of neural network used for unsupervised learning. In unsupervised learning, the model learns patterns from the data without using labeled outcomes. The goal is to find the underlying structure or representation of the data.

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samresume/ChronoGAN

This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs for time series generation..

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samresume/SeriesGAN

We introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs for time series generation.

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christopher-beckham/amr

Official adversarial mixup resynthesis repository

Language: Python - Size: 13.9 MB - Last synced at: about 1 month ago - Pushed at: about 5 years ago - Stars: 35 - Forks: 2

AlexDelitzas/fcdae-neural-signal-denoising

Code for the paper "Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders"

Language: Python - Size: 173 MB - Last synced at: 2 months ago - Pushed at: about 3 years ago - Stars: 4 - Forks: 1

curiousily/Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras

iPython notebook and pre-trained model that shows how to build deep Autoencoder in Keras for Anomaly Detection in credit card transactions data

Language: Jupyter Notebook - Size: 67.9 MB - Last synced at: 6 months ago - Pushed at: almost 6 years ago - Stars: 520 - Forks: 279

fg-research/lstm-ae-sagemaker

SageMaker implementation of LSTM-AE model for time series anomaly detection.

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Anshlulla/Melodify

Exploring Generative Music using Autoencoders

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ahmedgh970/adversarial-training

Adversarially Training of Autoencoders for Unsupervised Anomaly Segmentation

Language: Python - Size: 65.4 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 4 - Forks: 0

ahmedgh970/brain-anomaly-seg

Transformer-based Models for Unsupervised Anomaly Segmentation in Brain MR Images

Language: Python - Size: 120 KB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 20 - Forks: 3

hauntedcupoftea/vaani Fork of 7Zenox/vaani

A religion based question answering AI, developed in collaboration for a university course on Design, Thinking and Innovation as part of my bachelor's degree.

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OleksiiLatypov/Practical-Deep-Learning-with-PyTorch

DataRoot Labs Practical Deep Learning with PyTorch

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PetropoulakisPanagiotis/igae

State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping

Language: Python - Size: 45.3 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0

bvpsk/Variational-Auto-Encoder-VAE-

Implementing VAE in keras and training on CelebA dataset

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TristanThorn/uwaterloo-skin-cancer-segmentation

An initial phase segmentation using LinkNet on the skin lesion dataset managed by VISION AND IMAGE PROCESSING LAB, University of Waterloo. Public dataset on Kaggle at https://www.kaggle.com/datasets/mahmudulhasantasin/university-of-waterloo-skin-cancer-db-80-10-10/.

Language: Python - Size: 94.7 KB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 0 - Forks: 0