Research map / Label-efficient and robust learning / Recognition and long-tail
Top-Down Deep Clustering with Multi-Generator GANs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add refinement operations after raw splits (ablation) | HC-MGAN · MNIST | ACC: .877 -> .943 | Helped |
| add refinement operations after raw splits (ablation) | HC-MGAN · MNIST | NMI: .856 -> .905 | Helped |
| add refinement operations after raw splits (ablation) | HC-MGAN · FMNIST | ACC: .704 -> .721 | Helped |
| add refinement operations after raw splits (ablation) | HC-MGAN · FMNIST | NMI: .690 -> .691 | Helped |
| add refinement operations after raw splits (ablation) | HC-MGAN · SOP | ACC: .204 -> .229 | Helped |
| add refinement operations after raw splits (ablation) | HC-MGAN · SOP | NMI: .063 -> .072 | Helped |
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- ClusterGAN: Latent Space Clustering in Generative Adversarial Networks (AAAI 2019 · similarity 0.91)
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- MGAN: Training Generative Adversarial Nets with Multiple Generators (ICLR 2018 · similarity 0.88)
- On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses (NeurIPS 2022 · similarity 0.87)
- Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework (ICML 2024 · similarity 0.87)
- From Logits to Hierarchies: Hierarchical Clustering made Simple (ICML 2025 · similarity 0.86)
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