xiwenc1/RD-DPP

The code for the paper: RD-DPP: Rate-Distortion Theory meets Determinantal Point Process to Diversify Learning Data Samples

RD-DPP

The code for the paper: RD-DPP: Rate-Distortion Theory meets Determinantal Point Process to Diversify Learning Data Samples.

We currently provide:

• main_mnist.py: Train 3 layer CNN on MNIST.
• main_small.py: Train logistic regression on small datasets.
• main_cifar.py: Train for EfficientNet on CIFAR10.

show results:

• show_results_mnist.ipynb. Some results are in exp2_MNIST.
• show_results_small.ipynb. Some results are in exp_small_dataset.
• show_results_cifar2.ipynb. Some results are in exp_up_10_efficientnet.

Some results on CIFAR10 (Please View threenet_result.png.png)

DL Models are modified from: https://github.com/kuangliu/pytorch-cifar. Thanks!

K-center coreset is from: https://github.com/google/active-learning/tree/master. Thanks!