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GitHub topics: autoencoder-model

shobrook/sequitur

Library of autoencoders for sequential data

Language: Python - Size: 1.94 MB - Last synced at: 15 days ago - Pushed at: over 1 year ago - Stars: 441 - Forks: 56

itailang/geometric_adv

Geometric Adversarial Attacks and Defenses on 3D Point Clouds (3DV 2021)

Language: Python - Size: 12 MB - Last synced at: about 1 year ago - Pushed at: almost 2 years ago - Stars: 22 - Forks: 0

storieswithsiva/CNN-AutoEncoder-DeepLearning

βž•πŸ’“Let's build the Simplest Possible Autoencoder . β‰οΈπŸ·We'll start Simple, with a Single fully-connected Neural Layer as Encoder and as Decoder. πŸ‘¨πŸ»β€πŸ’»πŸŒŸAn Autoencoder is a type of Artificial Neural Network used to Learn Efficient Data Codings in an unsupervised mannerπŸŒ˜πŸ”‘

Language: Jupyter Notebook - Size: 45.5 MB - Last synced at: over 1 year ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1

StolikTomer/SAGA

SAGA: Spectral Adversarial Geometric Attack on 3D Meshes (ICCV 2023)

Language: Python - Size: 135 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 10 - Forks: 1

KacperWiniarski/CelebFaces

CelebFaces is my computer vision gym, where I solve different tasks, test different approaches and learn a lot.

Language: Jupyter Notebook - Size: 14.7 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

Djmcflush/RaveFussion

A text to audio pipeline using Riffusion (a finetuned stablediffusion model) and using RAVE a audio to audio AutoEncoder.

Language: Python - Size: 8.98 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 4 - Forks: 3

Goodsea/Richter-s-Eye

Richter's Predictor: Modeling Earthquake Damage Challenge 0.7521 Scored Solution

Language: Jupyter Notebook - Size: 33.4 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 7 - Forks: 4

kaledhoshme123/Colorize-Images-of-city-streets

Proposing a structure for a convolutional neural network capable of coloring grayscale images. The study focused on images of streets within cities. The generative neural network was trained on as many street images as possible.

Language: Jupyter Notebook - Size: 2.87 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0