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..
Diversity Matters When Learning From Ensembles
Authors: Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using standard image classification benchmarks, we empirically validate that our distillation method promotes diversities in student network predictions, leading to improved performance, especially in terms of uncertainty estimation. |
| Researcher Affiliation | Collaboration | KAIST1, Daejeon, South Korea, AITRICS2, Seoul, South Korea, Stanford University3, USA |
| Pseudocode | Yes | Algorithm 1 Knowledge distillation from deep ensembles with ODS perturbations |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We compared our methods on CIFAR-10/100 and Tiny Image Net. |
| Dataset Splits | Yes | (a) Diversity plots of DE-4 teachers for Res Net-32 on train examples of CIFAR-10. (b) Validation set |
| Hardware Specification | No | The paper does not explicitly specify the hardware (e.g., CPU, GPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The important hyperparameters for KD are the pair (α, τ); for CIFAR10, after a through hyperparameter sweep, we decided to stay consistent with the convention of (α, τ) = (0.9, 4) for all methods [Hinton et al., 2015, Cho and Hariharan, 2019, Wang et al., 2020]. For CIFAR-100 and Tiny Image Net, we used the value (α, τ) = (0.9, 1) for all methods. We fix ODS step-size η to 1/255 across all settings. |