The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning

Artyom Gadetsky, Maria Brbić

Project page | BibTeX

---

This repo contains the source code of the HUME algorithm written in PyTorch. HUME is a model-agnostic framework for inferring human labeling of a given dataset without any external supervision. For more details please check our paper The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning (NeurIPS '23).

Dependencies

The code is built with the following libraries:

• PyTorch 1.13.1
• learn2learn 0.1.7
• numpy
• scipy
• scikit-learn
• tqdm

Data Preparation

You can download the prepared representations that we used in our experiments by running:

wget https://brbiclab.epfl.ch/wp-content/uploads/2023/11/data.zip
unzip data.zip

You can download the tasks found by HUME for evaluation by running:

wget https://brbiclab.epfl.ch/wp-content/uploads/2023/11/tasks.zip
unzip tasks.zip

You can also use your own representations and datasets. HUME is compatible with any pretrained representations. The rule of thumb is to use self-supervised representations pretrained on the dataset of interest as $\phi_1$. Do not forget to normalize the representations $\phi_1$ to have unit norm (see Section 2.2 in the paper for the details). As the second representation space $\phi_2$ you can use any large pretrained model suitable for your dataset.

Training

To check the available hyperparameters you can run:

python hume.py --help

The default hyperparameters are set to correspond to the hyperparameters used on the STL-10, CIFAR-10 and CIFAR-100-20 datasets.

For example, to run HUME on CIFAR-10 in inductive setting with MOCOv2 self-supervised representations and DINOv2 pretrained representations, run:

python hume.py \
--phi1_path data/representations/mocov2/cifar10train_l2.npy \
--phi2_path data/representations/dino/cifar10train.npy \
--gt_labels_path data/labels/cifar10train_targets.npy \
--exp_path tasks/inductive/moco_dino/cifar10/ \
--k 10 \
--seed 42 # Choose random seed

For STL-10 and CIFAR-100-20 just change paths accordingly and set --k to the corresponding number of classes.

Similarly, to run the same experiment in transductive setting, run:

python hume.py \
--phi1_path data/representations/mocov2/cifar10traintest_l2.npy \
--phi2_path data/representations/dino/cifar10traintest.npy \
--gt_labels_path data/labels/cifar10traintest_targets.npy \
--exp_path tasks/transductive/moco_dino/cifar10/ \
--k 10 \
--seed 42 # Choose random seed

To run HUME on ImageNet-1000 in inductive setting with MOCOv2 self-supervised representations and DINOv2 pretrained representations, run:

python hume.py \
--phi1_path data/representations/mocov2/imagenet1000train_l2.npy \
--phi1_path_val data/representations/mocov2/imagenet1000test_l2.npy \
--phi2_path data/representations/dino/imagenet1000train.npy \
--phi2_path_val data/representations/dino/imagenet1000test.npy \
--gt_labels_path data/labels/imagenet1000test_targets.npy \
--exp_path tasks/inductive/moco_dino/imagenet1000/ \
--k 1000 \
--outer_lr 0.1 \
--inner_lr 0.1 \
--adaptation_steps 100 \
--subset_size 20000 \
--train_fraction 0.7 \
--no_anneal \
--no_rand_init \
--seed 42 # Choose random seed

Evaluation

To evaluate the obtained tasks use evaluate.py. For example, to evaluate 100 tasks obtained on CIFAR-10 in the inductive setting, run:

python evaluate.py \
--phi1_path data/representations/mocov2/cifar10test_l2.npy \
--tasks_path tasks/inductive/moco_dino/cifar10/ \
--gt_labels_path data/labels/cifar10test_targets.npy

Acknowledgements

While developing HUME we greatly benefited from the open-source repositories:

• SCAN
• SPICE
• Solo-learn
• Unified SSL Benchmark
• CLIP
• DINOv2
• BigTransfer
• MoCo

Citing

If you find our code useful, please consider citing:

@inproceedings{
    gadetsky2023pursuit,
    title={The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning},
    author={Gadetsky, Artyom and Brbi\'c, Maria},
    booktitle={Advances in Neural Information Processing Systems},
    year={2023},
}