Remember The Past - Dataset Distillation   

The official implementation of the NeurIPS'22 paper:

Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks
Zhiwei Deng, Olga Russakovsky
Princeton University
NeurIPS 2022

Highlights:

We highlight two major contributions: (1) memory-addressing formulation; (2) backpropagation through time with momentum on dataset distillation

Through memory-addressing formulation: 1) the size of compressed data does not necessarily grow linearly with the number of classes; 2) an overall higher compression rate with more effective distillation is achieved; and 3) more generalized queries are allowed beyond one-hot labels.

Installation

conda env create -f environment.yml
source activate RememberThePast

Training & testing

Back-propagation through time (BPTT) without memory addressing

bash run_scripts/bptt_efficient_compressor_minibatch.sh debug ConvNet compressors_bptt_interventions.yml 1 150 SGD 0.9 CIFAR10 10 10 1 1 none 0

Back-propagation through time (BPTT) with memory addressing

bash run_scripts/bptt_efficient_compressor_basis_minibatch.sh debug ConvNet compressors_bptt_interventions.yml 1 SGD 150 SGD 0.9 CIFAR10 10 32 16 2 0 1 1 l2 1e-4 none 0

To add data augmentations, change none to flip_rotate

The hyperparameters are tuned with validation_ratio=0.1. When set as 0, the training will use the test set directly.

Reference

@inproceedings{deng2022remember,
  title={Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks},
  author={Zhiwei Deng and Olga Russakovsky},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2022}
}