Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency
Authors: Liqian Ma, Xu Jia, Stamatios Georgoulis, Tinne Tuytelaars, Luc Van Gool
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | 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... |