Official Pytorch implementation of Meta Internal Learning

Last update: Aug 24, 2022

Related tags

Overview

Meta Internal Learning

This repository is the official implementation of Meta Internal Learning by Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf.

Project | arXiv | Code

Requirements

To install requirements:

pip install -r requirements.txt

Training on a dataset

Create a folder X containing the images. (see structure in data folder)
Determine how many iterations Y to train by scale (depends mostly on the size of the dataset, you may refer to the appendix for reference).
Run

python train.py --image-path X --batch-size 16 --visualize --niter Y --min-size 25 --checkname X_result --SAVE-MODEL --SAVE-IMGS

Generated images, tensorboard logs and trained models are stored in MetaInternalLearning/run/X_result.

The default input format is jpg. Use '--file-suffix png' for .png files.

Examples from paper -

Places-50 -

python train.py --image-path data/places_50 --batch-size 16 --visualize --niter 4000  --min-size 28 --checkname places_50_result --SAVE-MODEL --SAVE-IMGS

LSUN-50

python train.py --image-path data/lsun_50 --batch-size 16 --visualize --niter 5000  --checkname lsun_50_result --SAVE-MODEL --SAVE-IMGS

Valley dataset can be downloaded here - http://places2.csail.mit.edu/download.html (256x256 small images) and can be divided into subsets as mentioned in the paper.

V500 -

python train.py --image-path data/V500 --batch-size 16 --visualize --niter 25000 --min-size 25 --checkname v500_result --ar 1 --SAVE-MODEL

V2500 -

python train_dataset_parallel.py --image-path data/V2500 --batch-size 16 --niter 100000 --rec-weight 50 --min-size 25 --checkname v2500_result --ar 1 --SAVE-MODEL

V5000 -

python train_dataset_parallel.py --image-path data/V5000 --batch-size 16 --niter 150000 --rec-weight 50 --min-size 25 --checkname v5000_result --ar 1 --SAVE-MODEL

Applications

Coming soon!

Official Pytorch implementation of Meta Internal Learning

Related tags

Overview

Meta Internal Learning

Requirements

Training on a dataset

Examples from paper -

Applications

Owner

PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

Neurons Dataset API - The official dataloader and visualization tools for Neurons Datasets.

Hybrid Neural Fusion for Full-frame Video Stabilization

The-Secret-Sharing-Schemes - This interactive script demonstrates the Secret Sharing Schemes algorithm

Fast, flexible and fun neural networks.

基于pytorch构建cyclegan示例

Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

The project of phase's key role in complex and real NN

Revisting Open World Object Detection

Dynamic Multi-scale Filters for Semantic Segmentation (DMNet ICCV'2019)

A highly efficient and modular implementation of Gaussian Processes in PyTorch

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

University of Rochester 2021 Summer REU focusing on music sentiment transfer using CycleGAN

Reproduces ResNet-V3 with pytorch

This is a repository of our model for weakly-supervised video dense anticipation.

This is an official implementation for "AS-MLP: An Axial Shifted MLP Architecture for Vision".

Prior-Guided Multi-View 3D Head Reconstruction

UCSD Oasis platform

Official Implementation of 'UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers' ICLR 2021(spotlight)

Official code release for: EditGAN: High-Precision Semantic Image Editing