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..
Teaching a GAN What Not to Learn
Authors: Siddarth Asokan, Chandra Seelamantula
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The advantage of the reformulation is demonstrated by means of experiments conducted on MNIST, Fashion MNIST, Celeb A, and CIFAR-10 datasets. |
| Researcher Affiliation | Academia | Siddarth Asokan Robert Bosch Center for Cyber-Physical Systems Indian Institute of Science Bangalore, India EMAIL Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore, India EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on MNIST [28], Fashion-MNIST [29], Celeb A [30] and CIFAR-10 [31] datasets. |
| Dataset Splits | No | The paper describes how positive/negative classes and minority classes are constructed from the datasets, but it does not specify traditional training/validation/test dataset splits with exact percentages, sample counts, or citations to predefined splits for general model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | Yes | The GAN models are coded in Tensor Flow 2.0 [32]. |
| Experiment Setup | Yes | In all the cases, latent noise is drawn from a 100-dimensional standard Gaussian N(0100, I100). The ADAM optimizer [34] with learning rate η = 10 4 and exponential decay parameters for the first and second moments β1 = 0.50 and β2 = 0.999 is used for training both the generator and the discriminator. A batch size of 100 is used for all the experiments and all models were trained for 100 epochs, unless stated otherwise. |