DREAM-OOD

This is the source code accompanying the paper Dream the Impossible: Outlier Imagination with Diffusion Models by Xuefeng Du, Yiyou Sun, Xiaojin Zhu, and Yixuan Li

The codebase is heavily based on Stable Diffusion.

Ads

Check out our latent-based outlier synthesis papers in ICLR'22 VOS and ICLR'23 NPOS if you are interested!

Requirements

A suitable conda environment named dreamood can be created and activated with:

conda env create -f environment.yaml
conda activate dreamood

Dataset Preparation

ImageNet-100

• Download the full ImageNet dataset from the official website here.

• Preprocess the dataset to get ImageNet-100 by running:

python scripts/process_in100.py --outdir xxx

where "--outdir" specifies the address of the dataset you want to store.

CIFAR-100

• The dataloader will download it automatically when first running the programs.

OOD datasets

• The OOD datasets with ImageNet-100 as in-distribution are 4 OOD datasets from iNaturalist, SUN, Places, and Textures, which contain the de-duplicated concepts overlapped with ImageNet.
• The OOD datasets with CIFAR-100 as in-distribution are 5 OOD datasets, i.e., SVHN, PLACES365, LSUN, ISUN, TEXTURES.
• Please refer to Part 1 and 2 of the codebase here.