Energy-based Generative Adversarial Networks
Authors: Junbo Zhao, Michael Mathieu, Yann LeCun
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we study the training stability of EBGANs over GANs on a simple task of MNIST digit generation with fully-connected networks. We run an exhaustive grid search over a set of architectural choices and hyper-parameters for both frameworks. |
| Researcher Affiliation | Collaboration | Junbo Zhao, Michael Mathieu and Yann Le Cun Department of Computer Science, New York University Facebook Artificial Intelligence Research {jakezhao, mathieu, yann}@cs.nyu.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | Yes | In this section, we study the training stability of EBGANs over GANs on a simple task of MNIST digit generation with fully-connected networks. using the LSUN bedroom dataset (Yu et al., 2015) and the large-scale face dataset Celeb A under alignment (Liu et al., 2015). We trained EBGANs to generate high-resolution images on Image Net (Russakovsky et al., 2015). |
| Dataset Splits | No | The paper does not provide specific dataset split information, such as exact percentages, sample counts, or a detailed splitting methodology for reproducibility across all datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like Adam optimizer and Batch Normalization, but it does not provide specific version numbers for any ancillary software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | Formally, we specify the search grid in table 1. We impose the following restrictions on EBGAN models: (i)-using learning rate 0.001 and Adam (Kingma & Ba, 2014) for both G and D; (ii)-n Layer D represents the total number of layers combining Enc and Dec. For simplicity, we fix Dec to be one layer and only tune the Enc #layers; (iii)-the margin is set to 10 and not being tuned. Batch normalization (Ioffe & Szegedy, 2015) is applied after each weight layer, except for the generator output layer and the discriminator input layer (Radford et al., 2015). Training images are scaled into range [-1,1]. Correspondingly the generator output layer is followed by a Tanh function. Re LU is used as the non-linearity function. Initialization: the weights in D from N(0, 0.002) and in G from N(0, 0.02). The bias are initialized to be 0. |