PUGAN Dataset: PU Learning in GANs
- The PUGAN dataset is a curated collection of image benchmarks (e.g., MNIST, CIFAR-10, CAT) designed to evaluate PU-inspired GAN frameworks.
- It features a variety of datasets including grayscale, color, and high-resolution images, addressing challenges like limited samples and mode coverage.
- Empirical evaluations on these datasets show lower FID scores and improved convergence, underscoring the benefits of reinterpreting GAN discrimination as a PU classification problem.
The PUGAN dataset refers to the collection of image datasets utilized in the evaluation of the Positive-Unlabeled Generative Adversarial Network (PUGAN) framework, introduced as part of the paper of positive-unlabeled classification in GAN training settings (2002.01136). The underlying motivation is to empirically test the advantage of reframing the binary real–fake discrimination in GANs as a positive-unlabeled (PU) classification problem, thereby softening the conventional dichotomy and facilitating more stable adversarial training.
1. Role of Datasets in PUGAN Research
The datasets aggregated under the “PUGAN dataset” were selected to cover a diverse spectrum of image synthesis challenges, including simple multicategory grayscale images, more complex color images, and high-resolution scenarios with limited sample availability. The rationale for this selection is to rigorously examine the convergence behavior, training stability, and sample quality of PUGAN and its variants under varied generative modeling conditions. High-resolution datasets with relatively few samples (e.g., CAT-256) serve as a stress test for mode coverage and sample fidelity.
2. Dataset Composition and Characteristics
The primary datasets reported in the original PUGAN paper and leveraged in quantitative/qualitative evaluation are itemized below:
| Dataset | Type | Resolution(s) | Notable Aspects |
|---|---|---|---|
| MNIST | Grayscale | 32×32 | Handwritten digits, multi-class |
| FMNIST | Grayscale | 32×32 | Clothing images, multi-class |
| CIFAR-10 | Color | 32×32 | Object, animal categories, multi-class |
| CAT | Color | 64, 128, 256 | Limited samples, high-resolution |
| LSUN-bedroom | Color | 64, 128 | Scene, high intra-class variance |
| CelebA | Color | 128 | Face images, medium sample size |
- MNIST and FMNIST datasets underwent resizing to 32×32 to align with the architectural configurations.
- CAT-128 and CAT-256 have sample sizes of 6,645 and 2,011 images, respectively, posing significant challenges in data-limited high-resolution synthesis.
- LSUN-bedroom and CelebA provide benchmarks for scene and facial image generation at higher resolutions.
3. Dataset Utility in Evaluating Positive-Unlabeled GANs
The choice of datasets is critical for assessing the effectiveness of a PU learning-inspired discriminator, as the structure and complexity of the test corpora influence both convergence and sample fidelity. For instance, limited-sample, high-resolution domains (CAT-128/256) accentuate instability and mode collapse in baseline GANs, and their use directly demonstrates the stabilization claimed for PUGAN.
Empirical results indicate that PUGAN and its variants (PUSGAN, PUHingeGAN, PULSGAN, PUWGAN-GP) achieve lower Fréchet Inception Distance (FID) scores—indicating greater sample realism—than corresponding standard GAN baselines across all tested datasets. On CAT-256, PUGAN is able to converge and generate high-quality images where standard SGAN fails.
4. Implementation Protocols for the PUGAN Dataset
For experimental reproducibility, the paper details several preprocessing protocols:
- Resizing: All images are resized uniformly (e.g., 32×32 for MNIST/FMIST/CIFAR-10).
- Training/Test Split: Explicit splits are maintained, especially for high-resolution sets with few samples to avert overfitting.
- Data Augmentation: While not specified as a core experimental variable, standard augmentation strategies are recommended in practice for limited-sample, high-resolution domains.
A plausible implication is that the stabilization properties of PUGAN are independent of the domain structure but may be accentuated as the generative task difficulty increases (e.g., high-resolution synthesis).
5. Dataset-Driven Theoretical Modeling
The structure of these datasets aligns with the PU framework articulated in the PUGAN theory section. Generated (“fake”) data are interpreted as unlabeled, comprising a mixture of high-quality (real-like) and low-quality generated samples. The empirical diversity and resolution of the datasets ensure that the theoretical models (involving class priors, mixture modeling, and risk formulations) can be tested under both ideal and adversarial conditions.
Notably, the experimental design also allows for a controlled adjustment of the class prior parameter π (proportion of high-quality samples in generated set), enabling smooth scheduling and further convergence improvements.
6. Comparative Context and Significance
In the context of subsequent research, including EBA-AI (Saoud et al., 20 Jul 2025), the datasets initially used for PUGAN—most notably CAT, LSUN-bedroom, and CelebA—remain seminal in benchmarking generative quality and stability. Recent studies employ additional or newly curated underwater datasets (e.g., LSUI400, Oceanex, UIEB100), but the methodology pioneered in PUGAN’s use of positive-unlabeled discrimination provides a reference standard for sample realism metrics and the mitigation of training instability.
7. Future Directions and Dataset Expansion
While the published PUGAN paper focused on a core suite of image datasets, future adoption may include domain-specific datasets outside of natural images (e.g., biomedical, underwater, or climate simulations) to further interrogate PU-classifier-based GAN stabilization. The observed benefits in limited-sample and high-resolution image synthesis suggest promising applicability in scarce annotated domains.
In summary, the “PUGAN dataset” refers to the collective set of benchmark image datasets employed to validate the Positive-Unlabeled GAN framework. These datasets are pivotal for illustrating how reframing GAN discrimination criteria impacts both stability and quality, and they establish high-resolution, limited-sample synthesis as a critical empirical frontier for future research in GAN methodology.
