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..
Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Authors: Liqian Ma, Xu Jia, Stamatios Georgoulis, Tinne Tuytelaars, Luc Van Gool
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process. |
| Researcher Affiliation | Collaboration | 1KU-Leuven/PSI, TRACE (Toyota Res in Europe) 2KU-Leuven/PSI 3ETH Zurich 4Huawei Noah s Ark Lab |
| Pseudocode | No | The paper describes the framework and learning procedures but does not include a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the availability of its source code. |
| Open Datasets | Yes | We set up a controlled experiment on the MNIST-Single dataset, which is created based on the handwritten digits dataset MNIST Le Cun et al. (1998)... We carry out a synthetic real experiment for street view translation between GTA5 (Richter et al., 2016) and Berkeley Deep Drive (BDD) (Xu et al., 2017) datasets... The Large-scale Celeb Faces Attributes (Celeb A) dataset (Liu et al., 2015)... we use EMNIST (Cohen et al., 2017) |
| Dataset Splits | No | The MNIST-Single dataset consists of two different domains as shown in Fig. 4. For domain A of both training/test sets, the foreground and background are randomly set to black or white but different from each other. For domain B of training set, the foreground and background for digits from 0 to 4 are randomly assigned a color from red, green, blue , and the foreground and background for digits from 5 to 9 are fixed to red and green, respectively. For domain B of testing set, the foreground and background of all digits are randomly assigned a color from red, green, blue . |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and neural network architectures (VGG, Deeplab) but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Table 6: Network architecture and training parameters. Translation n1 n2 n3 Minibatch Learning rate λs λc Iteration Single-digit 1 4 5 8 1e-5 1e3 1e1 60k Multi-digit 3 4 5 8 1e-5 1e4 1e2 60k GTA5 BDD 3 4 5 3 1e-4 1e4 1e2 22k Face gender 3 4 5 8 1e-4 5e3 1e1 30k. We use the Adam Kingma & Ba (2015) optimizer with β1 = 0.5 and β2 = 0.999. The learning rate is polynomially decayed with a power of 0.9... |