P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection
Authors: Zhiwei Zhang, Shifeng Chen, Lei Sun
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |