rodgzilla/machine_learning_pytorch_deep_image_prior rodgzilla/machine_learning_pytorch_deep_image_prior

  • Stars
    star
    1
  • Language
    Jupyter Notebook
  • Created over 6 years ago
  • Updated over 6 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
rodgzilla/machine_learning_pytorch_deep_image_prior
github

rodgzilla

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Implementation of the denoising part of the "Deep Image Prior" paper in PyTorch. This implementation is also available as a Google Colaboratory notebook.

More Repositories

1

machine_learning_adversarial_examples

An implementation of the 'fast gradient sign method' from the paper 'Explaining and Harnessing Adversarial Examples'
Jupyter Notebook
52
star
2

machine_learning_denoising

A Keras implementation of the "Deep Image Prior" paper.
Jupyter Notebook
20
star
3

machine_learning_mandelbrot_neural_network

A training of various neural networks to approximate the mandelbrot fractal image.
Jupyter Notebook
19
star
4

teaching

Python
15
star
5

NVSM_pytorch

PoC of the "Neural Vector Space Model" on a bunch of wikipedia articles.
Jupyter Notebook
8
star
6

talk-slides

Slides (with LaTeX files) of the various talks I give (ML / DL / fun with coding)
TeX
8
star
7

buddhabrot

Basic buddhabrot fractal rendering in Python using multiprocessing and Python Image Library
Python
3
star
8

machine_learning_image_anomaly_detection

Program that tries do detect outliers in an image dataset
Jupyter Notebook
3
star
9

machine_learning_toy_autoencoder

A simple autoencoder trained on the MNIST dataset and a visualization of the latent code space.
Jupyter Notebook
3
star
10

machine_learning_car_counter

A small deep learning notebook based on the object detection model of Tensorflow that counts the number of cars on a picture.
Jupyter Notebook
2
star
11

machine_learning_deep_knn

Experiments with deep learning interpretability based on "Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning" (https://arxiv.org/abs/1803.04765)
Jupyter Notebook
2
star
12

fractal_GAN

A deep learning project where the goal is to train a generator to produce plausible fractal images.
Jupyter Notebook
2
star
13

keras_neural_style_transfer

Keras implementation of the original neural style transfer paper.
Jupyter Notebook
1
star
14

machine_learning_used_cars

Data visualization and predictions on a kaggle used car data set.
Jupyter Notebook
1
star
15

machine_learning_intent_classification

Jupyter Notebook
1
star
16

machine_learning_pokemon

Trying to predict the type of pokemon knowing its characteristics (based on a Kaggle dataset).
Jupyter Notebook
1
star
17

machine_learning_pytorch_simple_cnn_1d_nlp

The purpose of this notebook is to demonstrate how to build a simple one dimensionnal CNN to do text classification. The dataset used in this notebook is the "Twitter Sentiment Analysis" dataset. The goal of the network is to be able to predict whether a given has a positive or a negative sentiment.
Jupyter Notebook
1
star
18

machine_learning_fractal_super_resolution

Using deep learning to produce zooms on the Mandelbrot set using super-resolution networks.
Jupyter Notebook
1
star
19

random_projection_experiment

Experiments with random projection for similarity measurement and Johnson-Linderstrauss lemma.
Jupyter Notebook
1
star