No Modes Left Behind: Capturing the Data Distribution Effectively Using GANs
Authors: Shashank Sharma, Vinay Namboodiri
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate that the proposed method results in substantial improvements through its detailed analysis on toy and real datasets. The quantitative and qualitative results demonstrate that the proposed method improves the solution for the problem of missing modes and improves training of GANs. |
| Researcher Affiliation | Academia | Shashank Sharma, Vinay P. Namboodiri Dept. of Computer Science and Engineering Indian Institute of Technology, Kanpur |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We test our model extensively against natural images from popular datasets like Cifar10 (Krizhevsky 2009), Celeb A (Liu et al. 2015); and an unusual dataset, frames from a surveillance video, (Varadarajan and Odobez 2009). |
| Dataset Splits | No | The paper mentions using CIFAR 10, Celeb A, and a surveillance video dataset, but does not provide specific details on how these datasets were split into training, validation, and test sets (e.g., percentages or absolute counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper states: 'We provide details regarding the network architectures and the parameter settings that we have used for the experiments in the supplementary material.' It does not provide these specific details in the main text. |