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..
Online Knowledge Distillation with Diverse Peers
Authors: Defang Chen, Jian-Ping Mei, Can Wang, Yan Feng, Chun Chen3430-3437
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experimental results in this section to evaluate the performance of the proposed approach for image classification. Experimental results show that the proposed framework consistently gives better performance than state-of-the-art approaches without sacrificing training or inference complexity, demonstrating the effectiveness of the proposed two-level distillation framework. |
| Researcher Affiliation | Collaboration | Defang Chen,1,2 Jian-Ping Mei,3* Can Wang,1,2 Yan Feng,1,2 Chun Chen1,2 1College of Computer Science, Zhejiang University, Hang Zhou, China. 2ZJU-Lianlian Pay Joint Research Center. 3College of Computer Science, Zhejiang University of Technology, Hang Zhou, China. |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | Codes will be released once the paper is accepted. |
| Open Datasets | Yes | CIFAR-10 and CIFAR-100 (Krizhevsky and Hinton 2009) both contain 50,000/10,000 training/testing colored natural images with 32 × 32 pixels, which are drawn from 10/100 classes. Image Net-2012 (Russakovsky et al. 2015) is a more challenging dataset consisting of about 1.3 million training images and 50 thousand validation images from 1000 classes. |
| Dataset Splits | Yes | CIFAR-10 and CIFAR-100 (Krizhevsky and Hinton 2009) both contain 50,000/10,000 training/testing colored natural images with 32 × 32 pixels, which are drawn from 10/100 classes. Image Net-2012 (Russakovsky et al. 2015) is a more challenging dataset consisting of about 1.3 million training images and 50 thousand validation images from 1000 classes. |
| Hardware Specification | No | The paper mentions 'computing resources' in the Acknowledgments but does not provide specific hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific library versions). |
| Experiment Setup | Yes | We use stochastic gradient descent with Nesterov momentum for optimization and set the initial learning rate to 0.1, momentum to 0.9. For CIFAR-10/CIFAR-100 dataset, we set the mini-batch size to 128 and weight decay to 5 × 10−4. The learning rate is divided by 10 at 150 and 225 of the total 300 training epochs for these two datasets. For Image Net-2012 dataset, we set the mini-batch size to 256, the weight decay to 1 × 10−4, and the learning rate is divided by 10 at 30 and 60 of the total 90 training epochs. |