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..
Generative Adversarial Nets
Authors: Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. |
| Researcher Affiliation | Collaboration | Ian J. Goodfellow , Jean Pouget-Abadie , Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair , Aaron Courville, Yoshua Bengio D epartement d informatique et de recherche op erationnelle Universit e de Montr eal Montr eal, QC H3C 3J7... Ian Goodfellow is now a research scientist at Google, but did this work earlier as a Ude M student Jean Pouget-Abadie did this work while visiting Universit e de Montr eal from Ecole Polytechnique. Sherjil Ozair is visiting Universit e de Montr eal from Indian Institute of Technology Delhi Yoshua Bengio is a CIFAR Senior Fellow. |
| Pseudocode | Yes | Algorithm 1 Minibatch stochastic gradient descent training of generative adversarial nets. |
| Open Source Code | Yes | All code and hyperparameters available at http://www.github.com/goodfeli/adversarial |
| Open Datasets | Yes | We trained adversarial nets an a range of datasets including MNIST[21], the Toronto Face Database (TFD) [27], and CIFAR-10 [19]. |
| Dataset Splits | No | The paper mentions using a 'validation set' for cross-validation of the ฯ parameter for evaluation, but it does not specify concrete dataset split information (percentages, sample counts, or clear predefined splits) for training, validation, and testing needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions general computing resources like 'Compute Canada, and Calcul Qu ebec for providing computational resources'. |
| Software Dependencies | No | The paper mentions using 'Pylearn2 [11] and Theano [6, 1]' but does not provide specific version numbers for these software dependencies, which are necessary for reproducibility. |
| Experiment Setup | Yes | The generator nets used a mixture of recti๏ฌer linear activations [17, 8] and sigmoid activations, while the discriminator net used maxout [9] activations. Dropout [16] was applied in training the discriminator net... The number of steps to apply to the discriminator, k, is a hyperparameter. We used k = 1, the least expensive option, in our experiments... We used momentum in our experiments. |