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..
Patch-Wise Graph Contrastive Learning for Image Translation
Authors: Chanyong Jung, Gihyun Kwon, Jong Chul Ye
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the state-of-art results for the image translation thanks to the semantic encoding by the constructed graphs. Experimental results in five different datasets demonstrates the state-of-the-art performance by producing semantically meaningful graphs. |
| Researcher Affiliation | Academia | Chanyong Jung1, Gihyun Kwon1, Jong Chul Ye1, 2 1 Department of Brain and Bio Engineering, KAIST, Daejeon, Republic of Korea 2 Kim Jaechul Graduate School of AI, KAIST, Daejeon, Republic of Korea |
| Pseudocode | No | None found. The paper describes its methods but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | None found. The paper does not provide any statement or link regarding the public release of source code for the methodology. |
| Open Datasets | No | We verify our method using the five datasets as follows: horse zebra, Label Cityscape, map satellite, summer winter, and apple orange. All images are resized into 256 256 for training and testing. |
| Dataset Splits | No | All images are resized into 256 256 for training and testing. |
| Hardware Specification | No | None found. The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | None found. The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | All images are resized into 256 256 for training and testing. For the graph construction, we randomly sampled 256 different patches from the pre-trained VGG16 (Simonyan and Zisserman 2014) network in both of input and output images. We extract the dense feature from the three different layers (relu3-1, relu4-1, relu4-3layer) inside of the network. For the graph operation, we set the number of GNN hops as 2, and pooling number as 1. For the graph pooling, we downsampled nodes by 1/4. In other words, we have 256 nodes in the initial graph, and 64 nodes for the pooled graph. |