Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
Authors: Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments, We conduct experiments on two existing virtual try-on benchmark datasets (MPV [6] dataset and Deep Fashion [21] dataset) and our newly collected large-scale benchmark dataset for unpaired try-on, named UPT. |
| Researcher Affiliation | Collaboration | 1Shenzhen Campus of Sun Yat-Sen University, 2Huya Inc 3Ui T The Arctic University of Norway, 4Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our code will be available at PASTA-GAN. |
| Open Datasets | Yes | We conduct experiments on two existing virtual try-on benchmark datasets (i.e., the Deep Fashion [21] and the MPV [6] datasets) and our newly collected large-scale benchmark dataset for unpaired try-on, named UPT. |
| Dataset Splits | No | The paper states for the UPT dataset: "UPT is further split into a training set of 27,139 images and a testing set of 6,115 images." but does not explicitly mention a validation set split. For other datasets, general training and testing sets are mentioned without explicit split percentages or counts for all three stages. |
| Hardware Specification | Yes | Our PASTA-GAN is implemented using Py Torch [28] and is trained on 8 Tesla V100 GPUs. |
| Software Dependencies | No | The paper states: "Our PASTA-GAN is implemented using Py Torch [28]" but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | During training, the batch size is set to 96 and the model is trained for 4 million iterations with a learning rate of 0.002 using the Adam optimizer [16] with β1 = 0 and β2 = 0.99. The loss hyper-parameters λrec, λperc, and λmask are set to 40, 40, and 100, respectively. The hhyper-parameters for the random erasing probability α1 and α2 are set to 0.2 and 0.9, respectively. |