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..
Safe Distillation Box
Authors: Jingwen Ye, Yining Mao, Jie Song, Xinchao Wang, Cheng Jin, Mingli Song3117-3124
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments across various datasets and architectures demonstrate that, with SDB, the performance of an unauthorized KD drops significantly while that of an authorized gets enhanced, demonstrating the effectiveness of SDB. |
| Researcher Affiliation | Collaboration | Jingwen Ye1,2, Yining Mao1, Jie Song1, Xinchao Wang2, Cheng Jin3, Mingli Song1,4 1 Zhejiang University 2 National University of Singapore 3 Fudan University 4 Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies |
| Pseudocode | No | The paper describes the SDB framework and its strategies (key embedding, knowledge disturbance, knowledge preservation) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Two public datasets are employed in the experiments, including the CIFAR10 dataset and CIFAR100 dataset. |
| Dataset Splits | No | The paper mentions using CIFAR10 and CIFAR100 datasets for experiments but does not explicitly provide the train/validation/test split percentages or sample counts in the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | We used Py Torch framework for the implementation. |
| Experiment Setup | Yes | For optimizing the SDB models, we used stochastic gradient descent with momentum of 0.9 and learning rate of 0.1 for 200 epochs. For applying distillation, we set T = 4 for CIFAR10 dataset and T = 20 for CIFAR100 dataset. In the random key generation, we set λ = 0.5. In the knowledge disturbance, we set Tdis = 4 for CIFAR10 dataset and Tdis = 20 for CIFAR100 dataset. |