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..
Pseudo-spherical Knowledge Distillation
Authors: Kyungmin Lee, Hyeongkeun Lee
IJCAI 2022 | Venue PDF | 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 EMAIL |
| 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. |