Code for "The Intrinsic Dimension of Images and Its Impact on Learning" - ICLR 2021 Spotlight

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Deep Learningdimensions
Overview

dimensions

Estimating the instrinsic dimensionality of image datasets

Code for: The Intrinsic Dimensionaity of Images and Its Impact On Learning - Phillip Pope and Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein (ICLR 2021, spotlight)

Basenjis of Varying dimensionality

Environment

This code was developed in the following environment

conda create dimensions python=3.6 jupyter matplotlib scikit-learn pytorch==1.5.0 torchvision cudatoolkit=10.2 -c pytorch

To generate new data of controlled dimensionality with GANs, you must install:

pip install pytorch-pretrained-biggan

To use the shortest-path method (Granata and Carnevale 2016) you must also compile the fast graph shortest path code gsp (written by Jake VdP + Sci-Kit Learn)

cd estimators/gsp
python setup.py install

Generate data of controlled dimensionality

python generate_data/gen_images.py \
  --num_samples 1000 \
  --class_name basenji \
  --latent_dim 16 \
  --batch_size 100 \
  --save_dir samples/basenji_16

Estimate dimension of generated samples

To run the MLE (Levina and Bickel) estimator on the synthetic GAN data generated above:

python main.py \
    --estimator mle \
    --k1 25 \
    --single-k \
    --eval-every-k \
    --average-inverse \
    --dset  samples/basenji_16 \
    --max_num_samples 1000 \
    --save-path results/basenji_16.json

Use --estimators to try different estimators

Citation

If you find our paper or code useful, please cite our paper:

@inproceedings{DBLP:conf/iclr/PopeZAGG21,
  author    = {Phillip Pope and
               Chen Zhu and
               Ahmed Abdelkader and
               Micah Goldblum and
               Tom Goldstein},
  title     = {The Intrinsic Dimension of Images and Its Impact on Learning},
  booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
               Virtual Event, Austria, May 3-7, 2021},
  publisher = {OpenReview.net},
  year      = {2021},
  url       = {https://openreview.net/forum?id=XJk19XzGq2J},
  timestamp = {Wed, 23 Jun 2021 17:36:39 +0200},
  biburl    = {https://dblp.org/rec/conf/iclr/PopeZAGG21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Acknowledgements

We gratefully acknowledge use of the following codebases when developing our dimensionality estimators:

We also thank Prof. Vishnu Boddeti for clarifying comments on the graph-distance estimator.

Disclaimer

This code released as is. We will do our best to address questions/bugs, but cannot guarantee support.

Owner
Phil Pope
CS PhD Student @ University of Maryland, College Park. Machine learning. Previously @ HRL, Clarifai, New College of Florida
Phil Pope
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