Symbolic Graph Reasoning Meets Convolutions
Authors: Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show incorporating SGR significantly improves plain Conv Nets on three semantic segmentation tasks and one image classification task. |
| Researcher Affiliation | Collaboration | School of Intelligent Systems Engineering, Sun Yat-sen University 2Carnegie Mellon University 3 School of Data and Computer Science, Sun Yat-sen University 4Petuum Inc. |
| Pseudocode | No | The paper includes a diagram in Figure 2 showing 'Implementation details of one SGR layer' with boxes and operations, but it does not contain a formal pseudocode block or a section explicitly labeled 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate on three public benchmarks... Specifically, Coco-Stuff [4]... ADE20k [52]... PASCAL-Context [34]... We further conduct studies for image classification task on CIFAR-100 [21]. |
| Dataset Splits | Yes | Coco-Stuff [4]... including 9,000 for training and 1,000 for testing. ADE20k [52] consists of 20,210 images for training and 2,000 for validation... PASCAL-Context [34] includes 4,998 images for training and 5105 for testing... CIFAR-100 [21] consisting of 50K training images and 10K test images. |
| Hardware Specification | Yes | We conduct all experiments using Pytorch, 2 GTX TITAN X 12GB cards on a single server. |
| Software Dependencies | No | The paper states 'We conduct all experiments using Pytorch,' but it does not specify the version number of Pytorch or any other software dependencies. |
| Experiment Setup | Yes | Dl and Dc for feature dimensions... are thus set as 256... We adopt the standard SGD optimization... set the base learning rate to 2.5e-3 for newly initialized layers and 2.5e-4 for pretrained layers. We train 64 epochs for Coco-Stuff and PASCAL-Context, and 120 epochs for ADE20K dataset... the batch size is used as 6. The input crop size is set as 513 513. For CIFAR-100: We set Dl and Dc as 128. During training, we use a mini-batch size of 64 on two GPUs using a cosine learning rate scheduling [16] for 600 epochs. |