Official Pytorch implementation of Meta Internal Learning

Last update: Aug 24, 2022

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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

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Official Pytorch implementation of Meta Internal Learning

Related tags

Overview

Meta Internal Learning

Requirements

Training on a dataset

Examples from paper -

Applications

Owner

A simple python library for fast image generation of people who do not exist.

Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )

Supplementary code for SIGGRAPH 2021 paper: Discovering Diverse Athletic Jumping Strategies

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms

Neural Fixed-Point Acceleration for Convex Optimization

The PyTorch improved version of TPAMI 2017 paper: Face Alignment in Full Pose Range: A 3D Total Solution.

Mask2Former: Masked-attention Mask Transformer for Universal Image Segmentation in TensorFlow 2

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

NuPIC Studio is an all­-in-­one tool that allows users create a HTM neural network from scratch

PyTorch implementation of the paper Ultra Fast Structure-aware Deep Lane Detection

Universal Probability Distributions with Optimal Transport and Convex Optimization

Process text, including tokenizing and representing sentences as vectors and Applying some concepts like RNN, LSTM and GRU to create a classifier can detect the language in which a sentence is written from among 17 languages.

A tutorial on DataFrames.jl prepared for JuliaCon2021

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NuPIC Studio is an all-in-one tool that allows users create a HTM neural network from scratch