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..
Knowledge Distillation by On-the-Fly Native Ensemble
Authors: xu lan, Xiatian Zhu, Shaogang Gong
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations show that ONE improves the generalisation performance of a variety of deep neural networks more significantly than alternative methods on four image classification dataset: CIFAR10, CIFAR100, SVHN, and Image Net, whilst having the computational efficiency advantages. |
| Researcher Affiliation | Collaboration | Xu Lan1, Xiatian Zhu2, and Shaogang Gong1 1Queen Mary University of London 2Vision Semantics Limited |
| Pseudocode | Yes | Algorithm 1 Knowledge Distillation by On-the-Fly Native Ensemble |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | CIFAR10 [25]: A natural images dataset... CIFAR100 [25]: A similar dataset as CIFAR10... SVHN: The Street View House Numbers (SVHN) dataset... Image Net: The 1,000-class dataset from ILSVRC 2012 [28]... |
| Dataset Splits | Yes | CIFAR10 [25]: A natural images dataset that contains 50,000/10,000 training/test samples... CIFAR100 [25]: A similar dataset as CIFAR10 that also contains 50,000/10,000 training/test images... SVHN: The Street View House Numbers (SVHN) dataset consists of 73,257/26,032 standard training/text images and an extra set of 531,131 training images. Image Net: The 1,000-class dataset from ILSVRC 2012 [28] provides 1.2 million images for training, and 50,000 for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper states, 'We implemented all networks and model training procedures in Pytorch,' but does not provide a specific version number for Pytorch or any other software dependency. |
| Experiment Setup | Yes | We used the SGD with Nesterov momentum and set the momentum to 0.9. We deployed a standard learning rate schedule that drops from 0.1 to 0.01 at 50% training and to 0.001 at 75%. For the training budget, we set 300/40/90 epochs for CIFAR/SVHN/Image Net, respectively. We adopted a 3-branch ONE (m=2) design unless stated otherwise... Following [10], we set T = 3 in all the experiments. |