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..
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection
Authors: Zhiwei Zhang, Shifeng Chen, Lei Sun
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on CIFAR-10, MNIST, and FMNIST show that our method improves the performance of the student GAN by 2.44%, 1.77%, and 1.73% when compressing the computation at ratios of 24.45:1, 311.11:1, and 700:1, respectively. In this section, the proposed P-KDGAN is evaluated on the well-known CIFAR-10 [Krizhevsky, 2009], MNIST [Le Cun and Cortes, 2005] and FMNIST [Xiao et al., 2017] datasets. |
| Researcher Affiliation | Academia | Zhiwei Zhang1,2 , Shifeng Chen1 and Lei Sun2 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China 2School of Information and Electronics, Beijing Institute of Technology, China |
| Pseudocode | Yes | Algorithm 1 Progressive Knowledge Distillation with GANs |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code or provide a link to a code repository. |
| Open Datasets | Yes | In this section, the proposed P-KDGAN is evaluated on the well-known CIFAR-10 [Krizhevsky, 2009], MNIST [Le Cun and Cortes, 2005] and FMNIST [Xiao et al., 2017] datasets. |
| Dataset Splits | Yes | For the three experimental datasets, the training and testing partitions remain as default. every experiment on the CIFAR-10 dataset is trained with 5000 samples and tested with 10,000 samples. |
| Hardware Specification | Yes | All the reported results are implemented using the Py Torch framework [Paszke et al., 2017] on NVIDIA TITAN 2080Ti. |
| Software Dependencies | No | The paper mentions 'Py Torch framework' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In the experiments, the batch size and epoch are set to 1 and 500 respectively. Adam [Kingma and Ba, 2015] is used for training with a learning rate of 0.002. The intermediate layers in the teacher networks are set to 64-128-256 channels following the OCGAN [Perera et al., 2019]. The student networks in each dataset utilize intermediate representations with 8-16-64 channels, 2-4-8 channels and 1-2-4 channels respectively. |