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

Last update: Dec 14, 2022

Overview

pytorch-deep-generative-replay

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

Results

Continual Learning on Permutated MNISTs

Test precisions without replay (left), with exact replay (middle), and with Deep Generative Replay (right).

Continual Learning on MNIST-SVHN

Test precisions without replay (left), with exact replay (middle), and with Deep Generative Replay (right).

Generated samples from the scholar trained without any replay (left) and with Deep Generative Replay (right).

Training scholar's generator without replay (left) and with Deep Generative Replay (right).

Continual Learning on SVHN-MNIST

Test precisions without replay (left), with exact replay (middle), and with Deep Generative Replay (right).

Generated samples from the scholar trained without replay (left) and with Deep Generative Replay (right).

Training scholar's generator without replay (left) and with Deep Generative Replay (right).

Installation

$ git clone https://github.com/kuc2477/pytorch-deep-generative-replay
$ pip install -r pytorch-deep-generative-replay/requirements.txt

Commands

Usage

$ ./main.py --help
$ usage: PyTorch implementation of Deep Generative Replay [-h]
                                                          [--experiment {permutated-mnist,svhn-mnist,mnist-svhn}]
                                                          [--mnist-permutation-number MNIST_PERMUTATION_NUMBER]
                                                          [--mnist-permutation-seed MNIST_PERMUTATION_SEED]
                                                          --replay-mode
                                                          {exact-replay,generative-replay,none}
                                                          [--generator-z-size GENERATOR_Z_SIZE]
                                                          [--generator-c-channel-size GENERATOR_C_CHANNEL_SIZE]
                                                          [--generator-g-channel-size GENERATOR_G_CHANNEL_SIZE]
                                                          [--solver-depth SOLVER_DEPTH]
                                                          [--solver-reducing-layers SOLVER_REDUCING_LAYERS]
                                                          [--solver-channel-size SOLVER_CHANNEL_SIZE]
                                                          [--generator-c-updates-per-g-update GENERATOR_C_UPDATES_PER_G_UPDATE]
                                                          [--generator-iterations GENERATOR_ITERATIONS]
                                                          [--solver-iterations SOLVER_ITERATIONS]
                                                          [--importance-of-new-task IMPORTANCE_OF_NEW_TASK]
                                                          [--lr LR]
                                                          [--weight-decay WEIGHT_DECAY]
                                                          [--batch-size BATCH_SIZE]
                                                          [--test-size TEST_SIZE]
                                                          [--sample-size SAMPLE_SIZE]
                                                          [--image-log-interval IMAGE_LOG_INTERVAL]
                                                          [--eval-log-interval EVAL_LOG_INTERVAL]
                                                          [--loss-log-interval LOSS_LOG_INTERVAL]
                                                          [--checkpoint-dir CHECKPOINT_DIR]
                                                          [--sample-dir SAMPLE_DIR]
                                                          [--no-gpus]
                                                          (--train | --test)

To Run Full Experiments

# Run a visdom server and conduct full experiments
$ python -m visdom.server &
$ ./run_full_experiments

To Run a Single Experiment

# Run a visdom server and conduct a desired experiment
$ python -m visdom.server &
$ ./main.py --train --experiment=[permutated-mnist|svhn-mnist|mnist-svhn] --replay-mode=[exact-replay|generative-replay|none]

To Generate Images from the learned Scholar

$ # Run the command below and visit the "samples" directory
$ ./main.py --test --experiment=[permutated-mnist|svhn-mnist|mnist-svhn] --replay-mode=[exact-replay|generative-replay|none]

Note

I failed to find the supplementary materials that the authors mentioned in the paper to contain the experimental details. Thus, I arbitrarily chose a 4-convolutional-layer CNN as a solver in this project. If you know where I can find the additional materials, please let me know through the project's Github issue.

Reference

Continual Learning with Deep Generative Replay, arxiv:1705.08690

Author

Ha Junsoo / @kuc2477 / MIT License

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

Related tags

Overview

pytorch-deep-generative-replay

Results

Continual Learning on Permutated MNISTs

Continual Learning on MNIST-SVHN

Continual Learning on SVHN-MNIST

Installation

Commands

Usage

To Run Full Experiments

To Run a Single Experiment

To Generate Images from the learned Scholar

Note

Reference

Author

Owner

Junsoo Ha

Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage

The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).

Python Environment for Bayesian Learning

deep-prae

An open source library for face detection in images. The face detection speed can reach 1000FPS.

【CVPR 2021, Variational Inference Framework, PyTorch】 From Rain Generation to Rain Removal

An open-access benchmark and toolbox for electricity price forecasting

A general, feasible, and extensible framework for classification tasks.

Pseudo-rng-app - whos needs science to make a random number when you have pseudoscience?

[CVPRW 2022] Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

CS550 Machine Learning course project on CNN Detection.

DeepLab resnet v2 model in pytorch

Image marine sea litter prediction Shiny

Official repository of Semantic Image Matting

An image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testingAn image base contains 490 images for learning (400 cars and 90 boats), and another 21 images for testing

A deep learning library that makes face recognition efficient and effective

[CVPR 2021 Oral] Variational Relational Point Completion Network

CycleTransGAN-EVC: A CycleGAN-based Emotional Voice Conversion Model with Transformer

Fuzzing JavaScript Engines with Aspect-preserving Mutation

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.