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..
One-Sided Unsupervised Domain Mapping
Authors: Sagie Benaim, Lior Wolf
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint. 4 Experiments We compare multiple methods: the Disco GAN or the Cycle GAN baselines; the one sided mapping using Ldistance (A B or B A); the combination of the baseline method with Ldistance; the self distance method. |
| Researcher Affiliation | Collaboration | Sagie Benaim1 and Lior Wolf1,2 1The Blavatnik School of Computer Science , Tel Aviv University, Israel 2Facebook AI Research |
| Pseudocode | No | The paper describes the methods and loss functions in prose and mathematical notation, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our entire code is made publicly available at https://github.com/sagiebenaim/Distance GAN. |
| Open Datasets | Yes | The 3D car dataset [4] consists of rendered images of 3D cars... Similarly, the head dataset, [17], consists of 3D images of rotated heads... Celeb A [26, 14] was annotated for multiple attributes... We use the baseline Cycle GAN method as well as our methods in order to translate from Street View House Numbers (SVHN) [15] to MNIST [12]. |
| Dataset Splits | No | For the car2car experiment, the car dataset is split into two parts, one of which is used for A and one for B (It is further split into train and test set). This mentions train and test, but lacks specific percentages, counts, or a clear method for reproduction of the split. No explicit mention of a 'validation' set or its split details. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using architectures from Disco GAN and Cycle GAN, but does not provide specific version numbers for software dependencies or frameworks (e.g., TensorFlow, PyTorch, Python versions). |
| Experiment Setup | Yes | Table 1: Tradeoff weights for each experiment. For Disco GAN, we use a fixed weight configuration for all experiments, as shown in Tab. 1. |