Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
Authors: Sameera Ramasinghe, Moshiur Farazi, Salman H Khan, Nick Barnes, Stephen Gould
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the efficacy of our solution on a baseline c GAN (i.e., Pix2Pix) which lacks diversity, and show that by only modifying its training mechanism (i.e., with our proposed Pix2Pix-Geo), one can achieve more diverse and realistic outputs on a broad set of image-to-image translation tasks. ... In this section, we demonstrate the effectiveness of the proposed training scheme using qualitative and quantitative experiments. ... Table 1 depicts the quantitative results. As shown, our model exhibits a higher diversity and a higher realism on multiple datasets. In all the cases, we outperform our baseline by a significant margin. ... We conduct an ablation study to compare the different variants of the proposed technique. Table 2 depicts the results. |
| Researcher Affiliation | Collaboration | Sameera Ramasinghe The Australian National University, Data61-CSIRO sameera.ramasinghe@anu.edu.au Moshiur Farazi Data61-CSIRO Salman Khan Mohamed Bin Zayed University of AI Nick Barnes The Australian National University Stephen Gould The Australian National University |
| Pseudocode | Yes | Algorithm 1: Training algorithm |
| Open Source Code | Yes | Code available at: https://github.com/samgregoost/Rethinking-CGANs |
| Open Datasets | Yes | We validate the efficacy of our solution on a baseline c GAN (i.e., Pix2Pix) ... on a broad set of image-to-image translation tasks. ... We compare our method against state-of-the-art models that focus on multimodal image-to-image translation. Fig. 2 shows the qualitative results on landmarks faces, sketch anime and BW color. Table 1 depicts the quantitative results on 9 (nine) challenging datasets (e.g., facades2photo, sat2map, edges2shoes, edges2bags, sketch2anime, BW2color, lm2faces, hog2faces, night2day). |
| Dataset Splits | No | The paper does not explicitly specify training, validation, and test dataset splits with percentages or sample counts. While it mentions 'train' in Algorithm 1, it does not provide explicit details for a validation split. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments, such as specific GPU or CPU models, or cloud computing resources. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as 'Python 3.8' or 'PyTorch 1.9'. |
| Experiment Setup | No | The paper states 'For further details on the datasets and hyper-parameter settings, see App. I.', indicating that specific experimental setup details are provided in an appendix rather than in the main text. |