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..
Energy-based Generative Adversarial Networks
Authors: Junbo Zhao, Michael Mathieu, Yann LeCun
ICLR 2017 | Venue PDF | 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 EMAIL |
| 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. |