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 [1].
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. |