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..
IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks
Authors: Insu Jeon, Wonkwang Lee, Myeongjang Pyeon, Gunhee Kim7926-7934
AAAI 2021 | Venue PDF | 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]. |