Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound
Authors: Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Iranmanesh, Nasser Nasrabadi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments aim to show that an interpretable representation can be learned by the domain-specific variational information bound. Visual results on translation task show how domain-specific code can alter the style of generated images in a new domain. We compare our method against baselines both qualitatively and quantitatively. |
| Researcher Affiliation | Academia | Hadi Kazemi hakazemi@mix.wvu.edu Sobhan Soleymani ssoleyma@mix.wvu.edu Fariborz Taherkhani fariborztaherkhani@gmail.com Seyed Mehdi Iranmanesh seiranmanesh@mix.wvu.edu Nasser M. Nasrabadi nasser.nasrabadi@mail.wvu.edu West Virginia University Morgantown, WV 26505 |
| Pseudocode | No | The paper describes the framework's components and loss functions mathematically, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We use two datasets for qualitative comparison, edges handbags [36] and edges shoes [31]. Three other datasets, namely architectural labels photos from the CMP Facade database [28], and CUHK Face Sketch Dataset (CUFS) [27] are employed for more qualitative evaluation. |
| Dataset Splits | No | The paper mentions 'train', 'validation', and 'test' in the context of model stages and refers to using 'unpaired images', but it does not provide specific percentages, sample counts, or citations for dataset splits (e.g., 70% training, 15% validation, 15% test). |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer [14]' but does not provide specific version numbers for any key software components, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We use Adam optimizer [14] for online optimization with the learning rate of 0.0002. For reconstruction loss in (3), we set λ1 = 10 and λ2 = λ3 = 1. The values of α2 and α3 in (12) are set to 1, and the α4 α1 = β = 1. Finally, regarding the kernel parameter σ in (6), as discussed in [35], MMD is fairly robust to this parameter selection, and using 2 dim is a practical value in most scenarios, where dim is the dimension of vx1. |