Identifying Analogies Across Domains
Authors: Yedid Hoshen, Lior Wolf
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate our approach we conducted matching experiments on multiple public datasets. We have evaluated several scenarios: (i) Exact matches: Datasets on which all A and B domain images have... (ii) Partial matches:... (iii) Inexact matches:... (iv) Inexact point cloud matching:... |
| Researcher Affiliation | Collaboration | Yedid Hoshen1 and Lior Wolf1,2 1Facebook AI Research 2Tel Aviv University |
| Pseudocode | No | The paper describes the methodology using equations and textual explanations, but it does not include any clearly labeled pseudocode or algorithm blocks/figures. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology (AN-GAN) is publicly available. |
| Open Datasets | Yes | To evaluate our approach we conducted matching experiments on multiple public datasets. Facades: 400 images of building facades aligned with segmentation maps of the buildings (Radim Tyleˇcek, 2013). Maps: The Maps dataset was scraped from Google Maps by (Isola et al., 2017). E2S: The original dataset contains around 50K images of shoes from the Zappos50K dataset (Yu & Grauman, 2014), (Yu & Grauman). E2H: The original dataset contains around 137k images of Amazon handbags ((Zhu et al., 2016)). |
| Dataset Splits | No | The paper mentions using a 'training set' for the Maps dataset and refers to 'test set' accuracy/error for evaluation, but it does not specify the explicit percentages or counts for train/validation/test splits for any of the datasets used. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instances) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions several architectures and methods (e.g., Cycle GAN, VGG-16, U-net, Disco GAN, Pix2Pix) but does not specify version numbers for any software libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | Implementation: Initially β are all set to 0 giving all matches equal likelihood. We use an initial burn-in period of 200 epochs, during which δ = 0... We then optimize the examplar-loss for one α-iteration of 22 epochs, one T-iteration of 10 epochs and another α-iteration of 10 epochs... The initial learning rate for the exemplar loss is 1e 3 and it is decayed after 20 epochs by a factor of 2. We use the same architecture and hyper-parameters as Cycle GAN (Zhu et al., 2017) unless noted otherwise. |