Patch-Wise Graph Contrastive Learning for Image Translation
Authors: Chanyong Jung, Gihyun Kwon, Jong Chul Ye
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |