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 [1].
Guiding the One-to-One Mapping in CycleGAN via Optimal Transport
Authors: Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu4432-4439
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments indicate that the proposed algorithm is capable of learning a one-to-one mapping with the desired properties. We conduct several image-to-image translation experiments between different datasets, and we compare the translation results of our algorithm with Cycle GAN. |
| Researcher Affiliation | Academia | Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. The algorithm is described in prose within the 'Guiding Cycle GAN with Optimal Transport' section. |
| Open Source Code | No | The paper does not provide any specific links to source code or an explicit statement about its release. |
| Open Datasets | Yes | We conduct our first experiment between a car dataset (Fidler, Dickinson, and Urtasun 2012) and a chair dataset (Aubry et al. 2014). In this experiment, we performed image-to-image translation between a shoes dataset (Yu and Grauman 2014) and a handbags dataset (Zhu et al. 2016). |
| Dataset Splits | No | The paper mentions training models but does not specify the explicit splits for training, validation, or testing sets. It refers to datasets but not how they were partitioned for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'network simplex algorithm' and 'Adam optimizer' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We train our model for 3000 epochs with an initial learning rate of 0.0002 and linearly decayed it to zero. λgp is set as 10, λrec is set in the range of [100, 800] and λref is set in the range of [50, 300]. We train critic for 5 steps and generator for 1 step in turn. |