1. Low-Resolution Datasets:

   - The Pairwise Exposure Test folder contains files for conducting pairwise exposure tests on low resolution datasets.

2. High-Resolution Example:

   - Tumor-Detection-Test.ipynb: This Jupyter notebook allows you to reproduce the results on a tumor detection dataset. The notebook loads pre-generated outliers from a public Google Drive folder and trains the model using these exposures.

3. Out-of-Distribution (OOD) Example:

   - CIFAR10_OOD_Test.ipynb: We provide this Jupyter notebook to reproduce the results reported in Table 2.a of the paper. It loads pre-generated outliers from a public Google Drive folder and trains the model using these exposures and the hyperparameters specified in the paper.

4. Open-Set Recognition (OSR) Example:

   - MNIST_OSR_Test.ipynb: We provide this Jupyter notebook to reproduce the results reported in Table 2.b of the paper. It loads pre-generated outliers for 5 random selection of closed-set classes from MNIST dataset and trains the model using these exposures and the hyperparameters specified in the paper.

5. generate_exposures.ipynb

   - generate_exposures.ipynb: We provide this Jupyter Notebook to generate adaptive outliers. At first, we generate near-distribution prompts + negative adjective of normal label name.
     then we generate images by conditioning on prompts and images of normal class.

6. MNIST-OneClass-Test.ipynb

   - MNIST-OneClass-Test.ipynb : In this Jupyter Notebook we incorporate generated outliers and create a dataset with samples of the target class of MNIST as normal samples and these generated outliers as abnormal.
