Learning Generative Adversarial Networks from Multiple Data Sources
Authors: Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Phung
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and Image Net datasets and compute Fréchet Inception Distance to evaluate P2GAN s effectiveness in addressing the mode collapsing problem. |
| Researcher Affiliation | Collaboration | 1Faculty of Infomation Technology, Monash University, Australia 2AI Research Lab, Trusting Social, Australia 3Google Deep Mind |
| Pseudocode | No | The paper describes the model's formulation and training process in text and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'The supplementary material for this paper can be found at the following url address1. 1https://app.box.com/v/p2gan-supp.' However, it does not explicitly state that the source code for the methodology is provided within this supplementary material or elsewhere. |
| Open Datasets | Yes | We use CIFAR-10, STL-10, and Image Net datasets... CIFAR-10 contains 50,000 32 32 training images of 10 classes... STL-10 contains about 100,000 96 96 images... Image Net is the largest and most diverse datasets with more than 1.2 million images from 1,000 classes. |
| Dataset Splits | No | The paper mentions using CIFAR-10, STL-10, and ImageNet datasets and specific training image counts (e.g., 'CIFAR-10 contains 50,000 32 32 training images'), but it does not explicitly provide the specific training/validation/test dataset splits (e.g., percentages or sample counts for each split) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and specific divergences (Jensen-Shannon, KL), but it does not provide specific version numbers for any software dependencies, frameworks, or libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We use Adam optimizer with a batch size of 64. The learning rate and the first-order momentum are set at 0.0002 and 0.5, respectively. Regarding the pushing parameter α...we employed a gentle force of 0.01 for all experiment. We vary the total number of generators K in {1, 3, 5, 10, 15}... We add a new generator for every 15, 10, 5, 3 epochs... The learning process is terminated after 150 epochs for CIFAR-10, 100 epochs for STL-10, and 50 epochs for Image Net. |