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..
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
Authors: Jiawei Fan, Chao Li, Xiaolong Liu, Meina Song, Anbang Yao
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five mainstream benchmarks with various teacher-student network pairs demonstrate the effectiveness of our approach. Experimental results demonstrate that: (i) Af-DCD exhibits superior performance compared to state-of-the-art methods, on various benchmarks with different teacher-student network pairs; (ii) Af-DCD exhibits even more significant improvements on larger datasets, such as ADE20K, indicating it can enhance student s generalization capability. |
| Researcher Affiliation | Collaboration | Jiawei Fan Intel Labs China EMAIL Chao Li Intel Labs China EMAIL Xiaolong Liu Holo Matic Technology Co. Ltd. EMAIL Meina Song BUPT EMAIL Anbang Yao Intel Labs China EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/OSVAI/Af-DCD. |
| Open Datasets | Yes | Five popular semantic segmentation datasets, including Cityscapes [27], Pascal VOC [28], Camvid [29], ADE20K [30] and COCO-Stuff-164K [31], are used in our experiments. |
| Dataset Splits | Yes | Five popular semantic segmentation datasets, including Cityscapes [27], Pascal VOC [28], Camvid [29], ADE20K [30] and COCO-Stuff-164K [31], are used in our experiments. Following general settings [9, 20, 15] in semantic segmentation distillation... |
| Hardware Specification | Yes | The training time is measured on 8 NVIDIA RTX A5000 GPUs with 40000 iterations. |
| Software Dependencies | Yes | we implement our method on both MMSegmentation codebase [35] and CIRKD codebase [9]. |
| Experiment Setup | Yes | In training phase, all models are optimized by SGD with the momentum of 0.9, the initial learning rate of 0.02, and the batch size of 16. The input size is 512 × 1024, 400 × 400, 512 × 1024, 512 × 1024, for experiments on Pascal VOC, Cam Vid, ADE20K and COCO-Stuff-164K, respectively. The input size for experiments on Cityscapes are different in the two codebase, 512 × 1024 in CIRKD codebase and 512 × 512 in MMSegmentation codebase [15]. Our masked reconstruction generator consists of two 3 × 3 convolutional layers with Re LU, following [15]. |