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..
Latency-aware Spatial-wise Dynamic Networks
Authors: Yizeng Han, Zhihang Yuan, Yifan Pu, Chenhao Xue, Shiji Song, Guangyu Sun, Gao Huang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image classification, object detection and instance segmentation demonstrate that the proposed framework significantly improves the practical inference efficiency of deep networks. |
| Researcher Affiliation | Academia | Yizeng Han1 Zhihang Yuan2 Yifan Pu1 Chenhao Xue2 Shiji Song1 Guangyu Sun2 Gao Huang1 1 Department of Automation, BNRist, Tsinghua University, Beijing, China 2 School of Electronics Engineering and Computer Science, Peking University, Beijing, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Leap Lab THU/LASNet. |
| Open Datasets | Yes | The image classification experiments are conducted on the Image Net [4] dataset. ... We further evaluate our LASNet on the COCO [22] object detection task. |
| Dataset Splits | No | The paper mentions evaluating on the 'Image Net validation set' and conducting experiments on the 'Image Net [4] dataset' and 'COCO [22] object detection task', which implies standard splits for these public datasets are used. However, it does not explicitly provide specific percentages or sample counts for training/validation/test splits within the main text. |
| Hardware Specification | Yes | Various types of hardware platforms are tested, including a server GPU (Tesla V100), a desktop GPU (GTX1080) and edge devices (e.g., Nvidia Nano and Jetson TX2). |
| Software Dependencies | No | The paper mentions using 'torchvision pre-trained models' but does not specify software dependencies like PyTorch, CUDA, or other libraries with version numbers. |
| Experiment Setup | Yes | We fix α = 10, β = 0.5 and T = 4.0 for all dynamic models. More details are provided in Appendix B. ... finetune the whole network for 100 epochs ... finetuned on COCO with the standard setting for 12 epochs |