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..
Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition
Authors: Chuanguang Yang, XinQiang Yu, Han Yang, Zhulin An, Chengqing Yu, Libo Huang, Yongjun Xu
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on visual recognition tasks, including image classification, object detection, and semantic segmentation tasks, demonstrate that MTKD-RL achieves state-of-the-art performance compared to the existing multi-teacher KD works. |
| Researcher Affiliation | Academia | 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Overall MTKD-RL Training Procedure Algorithm 2: Alternative Multi-Teacher KD and Agent Optimization |
| Open Source Code | Yes | Code https://github.com/winycg/MTKD-RL |
| Open Datasets | Yes | We use CIFAR-100 (Krizhevsky, Hinton et al. 2009) and Image Net (Deng et al. 2009) datasets for image classification experiments. The object detection experiments adopt COCO-2017 (Lin et al. 2014) dataset. We utilize Cityscapes (Cordts et al. 2016), ADE20K (Zhou et al. 2017) and COCO-Stuff-164K (Caesar, Uijlings, and Ferrari 2018) datasets for semantic segmentation. |
| Dataset Splits | Yes | We use CIFAR-100 (Krizhevsky, Hinton et al. 2009) and Image Net (Deng et al. 2009) datasets for image classification experiments. The object detection experiments adopt COCO-2017 (Lin et al. 2014) dataset. We utilize Cityscapes (Cordts et al. 2016), ADE20K (Zhou et al. 2017) and COCO-Stuff-164K (Caesar, Uijlings, and Ferrari 2018) datasets for semantic segmentation. |
| Hardware Specification | Yes | As shown in Table 6(c), we compare training complexity in one epoch with other methods over NVIDIA RTX 4090. |
| Software Dependencies | No | No specific software versions (e.g., Python, PyTorch, CUDA versions) are mentioned. |
| Experiment Setup | Yes | We set α = 1 and β = 5, as analysed in Fig.2. At first, we pre-train the student network S by multi-teacher KD following LMT KD (Equ.(2)) with equal weights, i.e. {wm i = 1}M m=1, for one training epoch. |