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 [1].
Advancing Out-of-Distribution Detection via Local Neuroplasticity
Authors: Alessandro Canevaro, Julian Schmidt, Sajad Marvi, Hang Yu, Georg Martius, Julian Jordan
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach on benchmarks from image and medical domains, demonstrating superior performance and robustness compared to state-of-the-art techniques. |
| Researcher Affiliation | Collaboration | 1Mercedes-Benz AG, Sindelfingen, Germany 2University of T ubingen, T ubingen, Germany |
| Pseudocode | No | The paper describes the step-by-step procedure of the method in Section 2.2 using numbered paragraphs and mathematical formulas, but it does not present it in a formal pseudocode block or algorithm environment. |
| Open Source Code | Yes | Our code is publicly available at the following link: https: //github.com/alessandro-canevaro/KAN-OOD. |
| Open Datasets | Yes | We tested our method on seven different benchmarks from two different domains: the Open OOD CIFAR10, CIFAR-100, Image Net-200 full-spectrum (FS), and Image Net-1K FS (Yang et al., 2022) for the image domain, and the Ethnicity, Age, and Synthetic OOD benchmarks for the tabular medical data domain (Azizmalayeri et al., 2023). |
| Dataset Splits | Yes | We evaluate the KAN detector on the CIFAR-10 benchmark, using CIFAR10 (Krizhevsky et al., b) as the In D dataset. The OOD datasets are categorized into near OOD datasets (CIFAR-100 (Krizhevsky et al., a) and Tiny Image Net (TIN) (Le & Yang, 2015)) and far OOD datasets (MNIST (Deng, 2012), SVHN (Netzer et al., 2011), Textures (Cimpoi et al., 2014), and Places365 (Zhou et al., 2018)). |
| Hardware Specification | Yes | All experiments are performed on a single NVIDIA Ge Force GTX 1080Ti GPU. For testing larger models and accelerating the hyperparameter optimization, we leveraged a cloud computing platform with an NVIDIA A100 GPU. |
| Software Dependencies | Yes | We used Python version 3.10 together with Py Torch 2.3.1 as the deep learning framework and leveraged CUDA 11.8 for GPU acceleration. |
| Experiment Setup | Yes | In our experiments, we use a learning rate of 0.1 and limit the training to a single epoch. |