vlomonaco / Generative_Continual_Learning

Generative Models from the perspective of Continual Learning

Abstract

Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks.
We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tuning. We used two quantitative metrics to estimate the generation quality and memory ability. We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).
We found that among all models, the original GAN performs best and among Continual Learning strategies, generative replay outperforms all other methods. Even if we found satisfactory combinations on MNIST and Fashion MNIST, training generative models sequentially on CIFAR10 is particularly instable, and remains a challenge.

Example of generative tasks sequence and generation capability to reach.

Installation

Clone Repos

git clone https://github.com/TLESORT/Generation_Incremental.git

Create Set-up

Manual

pytorch 0.4
torchvision 0.2.1
imageio 2.2.0
tqdm 4.19.5

Conda environmnet

conda env create -f environment.yml
source activate py36

Docker environmnet

TODO

Experiments Done

Dataset

• MNIST
• Fashion MNIST

Generative Models

• GAN
• CGAN
• WGAN
• WGAN_GP
• VAE
• CVAE

Task

• Disjoint tasks -> 10 tasks

To Add

• Cifar10

Run experiments

cd Scripts
./generate_test.sh
./test_todo.sh

NB : Test todo will contains all bash commands to run since it may takes some days to run them all you can choose one of them manually and run it in the main repository Manual Example of commands for training and evaluating Generative_replay with GAN on Mnist :

Generate Data

cd ./Data
#For the expert
python main.py --task disjoint --dataset mnist --n_tasks 1 --dir ../Archives
#For the models to train
python main.py --task disjoint --dataset mnist --n_tasks 10 --dir ../Archives
#For Upperbound and FID
python main.py --task disjoint --upperbound True --dataset mnist --n_tasks 10 --dir ../Archives

# Go back to main repo
cd ..

Train Expert to compute later FID

python main.py --context Classification --task_type disjoint --method Baseline --dataset mnist --epochs 50 --epoch_Review 50 --num_task 1 --seed 0 --dir ./Archives

Train Generator

python main.py --context Generation --task_type disjoint --method Generative_Replay --dataset mnist --epochs 50 --num_task 10 --gan_type GAN --train_G True --seed 0 --dir ./Archives

Review Generator with Fitting Capacity

python main.py --context Generation --task_type disjoint --method Generative_Replay --dataset mnist --epochs 50 --num_task 10 --gan_type GAN --Fitting_capacity True --seed 0 --dir ./Archives

Review Generator with FID

python main.py --context Generation --task_type disjoint --method Generative_Replay --dataset mnist --epochs 50 --num_task 10 --gan_type GAN --FID True --seed 0 --dir ./Archives

print figures

Go to the main repository

Plot Fitting Capacity

python print_figures.py --fitting_capacity True

Plot FID

python print_figures.py --FID True

Fitting capacity at each task : GAN MNIST

Fashion-Mnist at each task results.

Plot Samples