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..
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse
Authors: Yang Li, Liangliang Shi, Junchi Yan
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on a single Ge Force RTX 3090. Synthetic data results are performed on Ge Force RTX 2080Ti. |
| Researcher Affiliation | Academia | Yang Li, Liangliang Shi and Junchi Yan Department of Computer Science and Engineering, Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper describes the proposed approach in detail, including equations and a loss function, but does not provide any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository. |
| Open Datasets | Yes | The experimented image datasets include MNIST [Le Cun et al., 1998], Stacked MNIST [Metz et al., 2017], CIFAR10 [Krizhevsky et al., 2009], STL-10 [Coates et al., 2011], LSUN [Yu et al., 2015] and CELEBA [Liu et al., 2015]. |
| Dataset Splits | No | The paper mentions using training and testing for different datasets but does not explicitly provide specific percentages, counts, or a detailed methodology for creating validation splits for reproducibility. |
| Hardware Specification | Yes | Experiments are conducted on a single Ge Force RTX 3090. Synthetic data results are performed on Ge Force RTX 2080Ti. |
| Software Dependencies | No | The paper describes the model architecture and training parameters, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set the loss weights as (λre, λGau) = (3, 10). Training batch size is set as 100 and we conduct 300 epochs for training. ... We set the loss weights as (λre, λGau) = (0.5, 0.1). ... The weights are set as (λre, λGau) = (3, 3). Latent space dimension is set as 100. ... All models are trained for 100K steps (mini-batches). We set (λre, λGau) = (0.01, 0.1). Latent space dimension is set as 128 and training batch size is set as 128. |