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..
Quality Aware Generative Adversarial Networks
Authors: KANCHARLA PARIMALA, Sumohana Channappayya
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate state-of-the-art performance using the Wasserstein GAN gradient penalty (WGAN-GP) framework over CIFAR-10, STL10 and Celeb A datasets. From the figures and tables, we see that QAGANs are very competitive with the state-of-the-art methods on all three datasets. |
| Researcher Affiliation | Academia | Parimala Kancharla, Sumohana S. Channappayya Department of Electrical Engineering Indian Institute of Technology Hyderabad EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It describes mathematical formulations and procedures in text and equations. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for their methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Datasets: We have evaluated the efficacy of proposed regularizers on three datasets: 1) CIFAR-10 [35] (60K images of 32 × 32 resolution), 2) Celeb A [Liu+15](202.6K face images cropped and resized to resolution 64 × 64. 3) STL-10 [CNL11] (100K images of resolution 96 × 96 and 48 × 48). |
| Dataset Splits | No | The paper mentions training on CIFAR-10, Celeb A, and STL-10 datasets and evaluation using Inception Score and FID, but it does not specify any training/validation/test dataset splits or how a validation set was specifically used. |
| Hardware Specification | No | The paper mentions a 'GPU donation' from NVIDIA, indicating GPUs were used, but it does not provide any specific details about the GPU model, CPU, memory, or other hardware specifications used for experiments. |
| Software Dependencies | No | The paper mentions using 'Adam as the optimizer' and 'TensorFlow' and 'Chainer' implementations for FID scores, but it does not specify version numbers for any of these software components. |
| Experiment Setup | Yes | We have used Adam as the optimizer with the standard momentum parameters β1 = 0. and β2 = 0.9. The initial learning rate was set to 0.0002 for CIFAR-10 and STL-10 datasets and 0.0001 for the Celeb A dataset. The learning rate is decreased adaptively. We have empirically chosen the hyper parameters λ1 and λ2 to be 1 and 0.1 respectively. All our models are trained for 100K iterations with a batch size of 64. The discriminator is updated five times for every update of the generator. |