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..
Few Shot Network Compression via Cross Distillation
Authors: Haoli Bai, Jiaxiang Wu, Irwin King, Michael Lyu3203-3210
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments n benchmark datasets demonstrate that cross distillation can significantly improve the student network s accuracy when only a few training instances are available. |
| Researcher Affiliation | Collaboration | 1The Chinese University of Hong Kong, 2Tencent AI Lab |
| Pseudocode | Yes | Algorithm 1 Cross distillation |
| Open Source Code | Yes | Our implementation in Py Torch is available at https://github.com/haolibai/ Cross-Distillation.git. |
| Open Datasets | Yes | evaluations are performed on CIFAR-10 and Image Net-ILSVRC12. |
| Dataset Splits | Yes | As we consider the setting of few shot image classification, we randomly select K-shot instances per class from the training set. All experiments are averaged over five runs with different random seeds, and results of means and standard deviations are reported. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with versions. |
| Experiment Setup | Yes | For Ours, we choose μ = 0.6 for VGG networks and μ = 0.9 for Res Nets. For Ours-S, we set (α, β) = (0.9, 0.3) on VGG networks and (0.9, 0.5) on Res Nets. Sensitivity analysis on these hyper-parameters are presented later. |