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..
Good Semi-supervised Learning That Requires a Bad GAN
Authors: Zihang Dai, Zhilin Yang, Fan Yang, William W. Cohen, Russ R. Salakhutdinov
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we derive a novel formulation based on our analysis that substantially improves over feature matching GANs, obtaining state-of-the-art results on multiple benchmark datasets. ... Empirically, our approach substantially improves over vanilla feature matching GANs, and obtains new state-of-the-art results on MNIST, SVHN, and CIFAR-10 ... 6 Experiments We mainly consider three widely used benchmark datasets, namely MNIST, SVHN, and CIFAR-10. ... Table 1: Comparison with state-of-the-art methods on three benchmark datasets. ... Table 2: Ablation study. |
| Researcher Affiliation | Academia | Zihang Dai , Zhilin Yang , Fan Yang, William W. Cohen, Ruslan Salakhutdinov School of Computer Science Carnegie Melon University dzihang,zhiliny,fanyang1,wcohen,EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/kimiyoung/ssl_bad_gan. |
| Open Datasets | Yes | We mainly consider three widely used benchmark datasets, namely MNIST, SVHN, and CIFAR-10. |
| Dataset Splits | Yes | As in previous work, we randomly sample 100, 1,000, and 4,000 labeled samples for MNIST, SVHN, and CIFAR-10 respectively during training, and use the standard data split for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | We add instance noise to the input of the discriminator [1, 18], and use spatial dropout [20] to obtain faster convergence. Except for these two modifications, we use the same neural network architecture as in [16]. We use the 10-quantile log probability to define the threshold ϵ in Eq. (4). |