IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks
Authors: Insu Jeon, Wonkwang Lee, Myeongjang Pyeon, Gunhee Kim7926-7934
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With the experiments on d Sprites and Color-d Sprites dataset, we demonstrate that IB-GAN achieves competitive disentanglement scores to those of state-of-the-art β-VAEs and outperforms Info GAN. |
| Researcher Affiliation | Academia | 1 Dept. of Computer Science and Engineering, Seoul National University, Republic of Korea (South) 2 School of Computing, Korea Advanced Institute of Science and Technology, Republic of Korea (South) |
| Pseudocode | Yes | Algorithm 1 IB-GAN training algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the open-source code for the described methodology. |
| Open Datasets | Yes | For quantitative evaluation, we measure the disentanglement metrics proposed in (Kim and Mnih 2018) on d Sprites (Higgins et al. 2017a) and Color-d Sprites (Burgess et al. 2018; Locatello et al. 2019) dataset. For qualitative evaluation, we visualize latent traversal results of IB-GAN and measure FID scores (Szegedy et al. 2015) on Celeb A (Liu et al. 2015) and 3D Chairs (Aubry et al. 2014) dataset. |
| Dataset Splits | No | The paper mentions using d Sprites and Color-d Sprites datasets and evaluating with a specific metric, but it does not provide explicit details about training, validation, or test splits such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions following DCGAN architecture with batch normalization and using RMSProp for optimization, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Optimization is performed with RMSProp (Tieleman and Hinton 2012) with a momentum of 0.9. The batch size is 64 in all experiments. We constrain true and synthetic images to be normalized as [ 1, 1]. |