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Coresets via Bilevel Optimization

This is the reference implementation for "Coresets via Bilevel Optimization for Continual Learning and Streaming" https://arxiv.org/pdf/2006.03875.pdf.

This repository also contains the implementation of the selection via Nyström proxy used for selecting batches in "Semi-supervised Batch Active Learning via Bilevel Optimization" https://arxiv.org/pdf/2010.09654. Selection via the Nyström proxy supports data augmentation, it is faster for larger coresets and hence supersedes the representer proxy in data summarization scenarios.

Overview

To get started with the library, check out demo.ipynb that shows how to build coresets for a toy regression problem and for MNIST classification. The following snippet outlines the general usage:

import bilevel_coreset
import loss_utils
import numpy as np

x, y = load_data()

# define proxy kernel function
linear_kernel_fn = lambda x1, x2: np.dot(x1, x2.T)

coreset_size = 10

coreset_constructor = bilevel_coreset.BilevelCoreset(outer_loss_fn=loss_utils.cross_entropy,
                                                    inner_loss_fn=loss_utils.cross_entropy,
                                                    out_dim=y.shape[1])
coreset_inds, coreset_weights = coreset_constructor.build_with_representer_proxy_batch(x, y, 
                                                    coreset_size, linear_kernel_fn, inner_reg=1e-3)
x_coreset, y_coreset = x[coreset_inds], y[coreset_inds]

Note: if you are planning to use the library on your problem, the most important hyperparameter to tune is inner_reg, the regularizer of the inner objective in the representer proxy - try the grid [10^-2, 10^-3, 10^-4, 10^-5, 10^-6].

Requirements

Python 3 is required. To install the required dependencies, run:

pip install -r requirements.txt

If you are planning to use the NTK proxy, consider installing the GPU version of JAX: instructions here. If you would like to run the experiments, add the project root to your PYTHONPATH env variable.

Data Summarization

Change dir to data_summarization. For running and plotting the MNIST summarization experiment, adjust the globals in runner.py to your setup and run:

python runner.py --exp cnn_mnist
python plotter.py --exp cnn_mnist

Similarly, for the CIFAR-10 summary for a version of ResNet-18 run:

python runner.py --exp resnet_cifar
python plotter.py --exp resnet_cifar

For running the Kernel Ridge Regression experiment, you first need to generate the kernel with python generate_cntk.py. Note: this implementation differs in the kernel choice in generate_kernel() from the paper. For details on the original kernel, please refer to the paper. Once you generated the kernel, generate the results by:

python runner.py --exp krr_cifar
python plotter.py --exp krr_cifar 

Continual Learning and Streaming

We showcase the usage our coreset construction in continual learning and streaming with memory replay. The buffer regularizer beta is tuned individually for each method. We provide the best betas from [0.01, 0.1, 1.0, 10.0, 100.0, 1000.0] for each method in cl_results/ and streaming_results/.

Running the Experiments

Change dir to cl_streaming. After this, you can run individual experiments, e.g.:

python cl.py --buffer_size 100 --dataset splitmnist --seed 0 --method coreset --beta 100.0

You can also run the continual learning and streaming experiments with grid search over beta on datasets derived from MNIST by adjusting the globals in runner.py to your setup and running:

python runner.py --exp cl
python runner.py --exp streaming
python runner.py --exp imbalanced_streaming

The table of result can be displayed by running python process_results.py with the corresponding --exp argument. For example, python process_results.py --exp imbalanced_streaming produces:

The experiments derived from CIFAR-10 can be similarly run by:

python cifar_runner.py --exp cl
python process_results --exp splitcifar
python cifar_runner.py --exp imbalanced_streaming
python process_results --exp imbalanced_streaming_cifar

Selection via the Nyström proxy

The Nyström proxy was proposed to support data augmentations. It is also faster for larger coresets than the representer proxy. An example of running the selection on CIFAR-10 can be found in batch_active_learning/nystrom_example.py.

Citation

If you use the code in a publication, please cite the paper:

@article{borsos2020coresets,
      title={Coresets via Bilevel Optimization for Continual Learning and Streaming}, 
      author={Zalán Borsos and Mojmír Mutný and Andreas Krause},
      year={2020},
      journal={arXiv preprint arXiv:2006.03875}
}