Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
Authors: Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi, Arshia Soltani Moakhar, Mohammad Azizmalayeri, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate RODEO in both clean and adversarial settings. In the adversarial setting, numerous strong attacks, including PGD-1000 (Madry et al., 2017), Auto Attack (Croce & Hein, 2020), and Adaptive Auto Attack (A3) (Liu et al., 2022), are employed for robustness evaluation. Our experiments are conducted across various common outlier detection setups, including Novelty Detection (ND), Open-Set Recognition (OSR), and Out-of-Distribution (OOD) detection. |
| Researcher Affiliation | Academia | 1Sharif University of Technology, Tehran, Iran 2Isfahan University of Technology, Isfahan, Iran. |
| Pseudocode | Yes | Algorithm 1 RODEO: Adversarial Training with Adaptive Exposure Dataset |
| Open Source Code | Yes | For access to the implementation, please visit the following link: https://rohban-lab.github.io/rodeo. |
| Open Datasets | Yes | For the low-resolution datasets, we included CIFAR10 (Krizhevsky et al., 2009), CIFAR100 (Krizhevsky et al., 2009), MNIST (Le Cun & Cortes, 2010), and Fashion MNIST (Xiao et al., 2017). Furthermore, we performed experiments on medical and industrial high-resolution datasets, namely Head-CT (Kitamura, 2018), MVTec-ad (Bergmann et al., 2019), Brain-MRI (Bhuvaji et al., 2020), Covid19 (Cohen et al., 2020), and Tumor Detection (Nickparvar, 2021). |
| Dataset Splits | Yes | Open-Set Recognition. For this task, each dataset was randomly split into an inlier set and an outlier set at a 60/40 ratio. This random splitting was repeated 5 times. |
| Hardware Specification | Yes | Experiments were conducted on RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions using Res Net-18, Wide Res Net, and Adam optimizer, citing their original papers. However, it does not provide specific version numbers for any software libraries or frameworks used (e.g., Python, PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | Furthermore, RODEO is trained 100 epochs with Adam(Kingma & Ba, 2017) optimizer with a learning rate of 0.001 for each experiment. We set the value of ϵ to 8 255 for low-resolution datasets and 2 255 for high-resolution ones. For the PGD attack, we use 10 random restarts for the attack, with random initializations within the range of ( ϵ, ϵ), and perform N = 1000 steps. Furthermore, we select the attack step size as ³ = 2.5 ϵ N . |