Unsupervised Learning under Latent Label Shift

A new approach to unsupervised learning leveraging domain structure and invariance.

NeurIPS 2022

Paper as Roberts*, Mani*, Garg, and Lipton.

ICML 2022 SCIS Workshop

Paper as Mani*, Roberts*, Garg, and Lipton.

SlidesLive Poster Session Video

Authors

Pranav Mani^*1 pmani@andrew.cmu.edu

Manley Roberts^*1 manleyroberts@cmu.edu, manley@abacus.ai

Saurabh Garg¹ sgarg2@andrew.cmu.edu

Zachary C. Lipton¹ zlipton@cmu.edu

*: Denotes equal contribution 1: Machine Learning Department, Carnegie Mellon University

Use Instructions

• Install a recent version of Python 3.
• pip install -r requirements.txt
• Install ImageNet by the instructions at https://www.image-net.org/download.php and replace 'root folder' in ImageNet and ImageNetSubset classes in dataset.py with the root folder of the installation (one level above the train/validation split folders). The test dataset we use is composed of the validation dataset from ImageNet, the validation dataset is split out of the train dataset of ImageNet.
• Details on downloading the FieldGuide dataset can be found here https://sites.google.com/view/fgvc6/competitions/butterflies-moths-2019. Extract images from training.rar into '~/FieldGuideAllImagesDownload/'. Then run ./data_utils/create_FieldGuide_directories.ipynb to create the FieldGuide-28 and FieldGuide-2 train, val and test directories.
• In experiment_config.yml, replace "project" and "entity" with the appropriate project and entity for WandB.

Attributions

Attributions are available in LICENSE_ATTRIBUTION