Research map / Label-efficient and robust learning / Recognition and long-tail
Balanced Self-Paced Learning for Generative Adversarial Clustering Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the adversarial GAN architecture and loss (ablation) | ClusterGAN · MNIST and CIFAR-10 | ACC | Helped |
| Add balanced self-paced learning (ablation) | ClusterGAN · MNIST and CIFAR-10 | ACC | Helped |
| Replace balanced self-paced learning with standard self-paced learning (ablation) | ClusterGAN · MNIST and CIFAR-10 | ACC | Hurt |
| Add the consistency loss (ablation) | ClusterGAN · CIFAR-10 | ACC | Helped |
| Add the consistency loss (ablation) | ClusterGAN · MNIST | ACC | Helped |
Closest papers by meaning
- ClusterGAN: Latent Space Clustering in Generative Adversarial Networks (AAAI 2019 · similarity 0.91)
- Top-Down Deep Clustering with Multi-Generator GANs (AAAI 2022 · similarity 0.90)
- On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses (NeurIPS 2022 · similarity 0.88)
- Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework (ICML 2024 · similarity 0.85)
- On the Adversarial Robustness of Multi-Kernel Clustering (ICML 2025 · similarity 0.84)
- Learning from Sample Stability for Deep Clustering (ICML 2025 · similarity 0.83)
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