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..
Debiased Distillation for Consistency Regularization
Authors: Lu Wang, Liuchi Xu, Xiong Yang, Zhenhua Huang, Jun Cheng
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the CIFAR-100, Image Net-1K, and Tiny-Image Net datasets validate the superiority of IKD. |
| Researcher Affiliation | Academia | Lu Wang 1, Liuchi Xu 1, Xiong Yang 2, Zhenhua Huang* 3, Jun Cheng* 4,5 1 School of Computer Science and Engineering, Northeastern University, Shenyang, China; 2 Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area, Shenzhen Polytechnic University, Shenzhen, China; 3 School of Computer Science, South China Normal University, Guangzhou, China; 4 Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, CAS, China; 5 The Chinese University of Hong Kong, Hong Kong, China 1 {wanglu@mail,xuliuchi@stumail}.neu.edu.cn, 2 EMAIL, 3 EMAIL, 4,5 EMAIL |
| Pseudocode | Yes | Algorithm 1 presents the pseudo-code of the IKD. Please refer to Supplementary Materials for a detailed algorithm description. |
| Open Source Code | Yes | Code https://github.com/yema-web/IKD |
| Open Datasets | Yes | CIFAR-100 (Krizhevsky, Hinton et al. 2009) comprises 100 classes, with each image having a resolution of 32 32 pixels. The CIFAR-100 dataset contains 50k training images and 10k validation images. Image Net-1K (ILSVRC2012) (Deng et al. 2009) is a large-scale dataset comprising 1k classes. The dataset comprises 1.2 million training images and 50k validation images. Tiny-Image Net (Le and Yang 2015) is a subset of the Image Net-1K dataset, consisting of 200 classes, and the image is 64 64 pixels. The training set contains 100k images, and the validation set contains 10k images. |
| Dataset Splits | Yes | CIFAR-100 (Krizhevsky, Hinton et al. 2009) ... The CIFAR-100 dataset contains 50k training images and 10k validation images. Image Net-1K (ILSVRC2012) (Deng et al. 2009) ... The dataset comprises 1.2 million training images and 50k validation images. Tiny-Image Net (Le and Yang 2015) ... The training set contains 100k images, and the validation set contains 10k images. |
| Hardware Specification | No | Implementations details are provided in Supplementary Materials due to page constraints. |
| Software Dependencies | No | Implementations details are provided in Supplementary Materials due to page constraints. |
| Experiment Setup | Yes | We conduct extensive ablation studies on the intra-class temperature T and the weight of IKD λavg. ... as shown in Fig. 4 (a), choosing a temperature parameter of 1.0 is appropriate for suppressing noise. ... Fig. 4 (b) demonstrates that for DKD+IKD, the optimal performance is achieved with a λavg of 6.5, whereas for NKD+IKD, a λavg of 6.0 yields the best results. For other parameter details, refer to Supplementary Materials. |