Research map / Graph learning / Graph self-supervised learning
Zero Shot Learning via Multi-Scale Manifold Regularization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add SGW regularization to Word2Vec semantic representations (ablation) | Zero-shot learning on AWA · AWA | unsupervised classification accuracy: 36% -> 80% | Helped |
| Add SGW regularization to Word2Vec semantic representations (ablation) | Zero-shot learning on CUB · CUB | unsupervised classification accuracy: 13 % -> 35% | Helped |
| Use the proposed method with Affinity Propagation instead of UDA (A+W) | Zero-shot learning on AWA · AWA | classification accuracy: 75.6% -> 81.3% | Helped |
| Use the proposed method instead of LatEm with Word2Vec | Zero-shot learning on CUB · CUB | classification accuracy: 33.1.% -> 35 % | Helped |
Closest papers by meaning
- TPR: Topology-Preserving Reservoirs for Generalized Zero-Shot Learning (NeurIPS 2024 · similarity 0.87)
- Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images (ICLR 2025 · similarity 0.86)
- Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration (NeurIPS 2025 · similarity 0.86)
- LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings (NeurIPS 2024 · similarity 0.85)
- Graph Geometry-Preserving Autoencoders (ICML 2024 · similarity 0.85)
- S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment (AAAI 2024 · similarity 0.85)
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