Quality Aware Generative Adversarial Networks
Authors: KANCHARLA PARIMALA, Sumohana Channappayya
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 {ee15m17p100001, sumohana}@iith.ac.in |
| 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. |