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..
Strengthen Out-of-Distribution Detection Capability with Progressive Self-Knowledge Distillation
Authors: Yang Yang, Haonan Xu
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results from multiple OOD scenarios verify the effectiveness and general applicability of PSKD. |
| Researcher Affiliation | Academia | 1Nanjing University of Science and Technology. Correspondence to: Yang Yang <EMAIL>. |
| Pseudocode | Yes | The pseudo code of PSKD is available in Appendix A. |
| Open Source Code | Yes | The code for this work is available at https://github. com/njustkmg/ICML25-PSKD |
| Open Datasets | Yes | Datasets. For small-scale experimental setups, CIFAR10/100 (Krizhevsky, 2009) is adopted as the ID dataset. ... Large-scale experiments are conducted on Image Net-200, a 200-class subset of Image Net-1K (Deng et al., 2009), as the ID dataset. |
| Dataset Splits | Yes | For validation, 1000 samples from the official test set are used, while the remainder are for testing. The model s self-selection mechanism is applied after the validation step at the end of each epoch. |
| Hardware Specification | Yes | All experiments are conducted using Python 3.8.19 and Py Torch 2.0.1 on a workstation equipped with dual 2.20 GHz CPUs, 384 GB of RAM, and six NVIDIA RTX 4090 GPUs. |
| Software Dependencies | Yes | All experiments are conducted using Python 3.8.19 and Py Torch 2.0.1 on a workstation equipped with dual 2.20 GHz CPUs, 384 GB of RAM, and six NVIDIA RTX 4090 GPUs. |
| Experiment Setup | Yes | Models are trained with stochastic gradient descent (SGD) for 100 epochs, using a learning rate of 0.1 with cosine annealing decay schedule (Loshchilov & Hutter, 2017), momentum of 0.9, and weight decay of 5 × 10−4. The batch size is set to 128 for CIFAR-10/100 and 256 for Image Net-200. |