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

Implementations for the ICLR-2021 paper: SEED: Self-supervised Distillation For Visual Representation.

Code for paper: Towards Tokenized Human Dynamics Representation

Robotic Process Automation in Windows and Linux by using Driagrams.net BPMN diagrams.

Principled Detection of Out-of-Distribution Examples in Neural Networks

Multi-Task Learning as a Bargaining Game

PyTorch implementation of "Continual Learning with Deep Generative Replay", NIPS 2017

Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

Implementation of "RaScaNet: Learning Tiny Models by Raster-Scanning Image" from CVPR 2021.

CVPR2021 Content-Aware GAN Compression

MAU: A Motion-Aware Unit for Video Prediction and Beyond, NeurIPS2021

My implementation of Fully Convolutional Neural Networks in Keras

Official implementation of VQ-Diffusion

NVIDIA container runtime

BirdCLEF 2021 - Birdcall Identification 4th place solution

TLoL (Python Module) - League of Legends Deep Learning AI (Research and Development)

CausalNLP is a practical toolkit for causal inference with text as treatment, outcome, or "controlled-for" variable.

Augmented CLIP - Training simple models to predict CLIP image embeddings from text embeddings, and vice versa.

Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

Official implementation of the paper: "LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech"

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

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