Pseudo-spherical Knowledge Distillation
Authors: Kyungmin Lee, Hyeongkeun Lee
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on various model compression tasks, we validate the effectiveness of our method by showing its superiority over the original knowledge distillation. |
| Researcher Affiliation | Academia | Kyungmin Lee , Hyeongkeun Lee Agency for Defense Development {kyungmnlee, lhk528}@gmail.com |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions using pre-trained teacher models from an official CRD repository (https://github.com/Hobbit Long/Rep Distiller), but does not explicitly state that the code for their own method (PSKD) is open-source or provide a link to it. |
| Open Datasets | Yes | Through experiments on CIFAR-100 and Image Net model compression benchmarks, we demonstrate the effectiveness of our new distillation methods. |
| Dataset Splits | Yes | Table 4: Top-1 and Top-5 error rates (%) of student network Res Net-18 on Image Net validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions using 'official Py Torch pretrained Res Net-34' but does not specify the version of PyTorch or any other software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions following model compression benchmarks from CRD [Tian et al., 2019] and using balancing weights α, β, η > 0, and specific γ values (0.5, 1.0), but does not provide explicit details like learning rates, batch sizes, number of epochs, or optimizer settings for the experimental setup. |