# TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning

arXiv: https://arxiv.org/abs/1908.11503 · HTML: https://mlsanity.com/paper/1908.11503/

## Key facts

- Cited by 10 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 10 of 10)

- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2309.00923/) (2023)
- [Bias-Eliminated Semantic Refinement for Any-Shot Learning](https://mlsanity.com/paper/2202.04827/) (2022)
- [Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning](https://mlsanity.com/paper/2212.13151/) (2022)
- [Recognizing Unseen States of Unknown Objects by Leveraging Knowledge Graphs](https://mlsanity.com/paper/2307.12179/) (2023)
- [A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios](https://mlsanity.com/paper/2302.04060/) (2023)
- [Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification](https://mlsanity.com/paper/2311.01212/) (2023)
- [G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification](https://mlsanity.com/paper/2608.26744/) (2026)

## Abstract (arXiv)

Zero-shot and few-shot learning aim to improve generalization to unseen concepts, which are promising in many realistic scenarios. Due to the lack of data in unseen domain, relation modeling between seen and unseen domains is vital for knowledge transfer in these tasks. Most existing methods capture seen-unseen relation implicitly via semantic embedding or feature generation, resulting in inadequate use of relation and some issues remain (e.g. domain shift). To tackle these challenges, we propose a Transferable Graph Generation (TGG) approach, in which the relation is modeled and utilized explicitly via graph generation. Specifically, our proposed TGG contains two main components: (1) Graph generation for relation modeling. An attention-based aggregate network and a relation kernel are proposed, which generate instance-level graph based on a class-level prototype graph and visual features. Proximity information aggregating is guided by a multi-head graph attention mechanism, where seen and unseen features synthesized by GAN are revised as node embeddings. The relation kernel further generates edges with GCN and graph kernel method, to capture instance-level topological structure while tackling data imbalance and noise. (2) Relation propagation for relation utilization. A dual relation propagation approach is proposed, where relations captured by the generated graph are separately propagated from the seen and unseen subgraphs. The two propagations learn from each other in a dual learning fashion, which performs as an adaptation way for mitigating domain shift. All components are jointly optimized with a meta-learning strategy, and our TGG acts as an end-to-end framework unifying conventional zero-shot, generalized zero-shot and few-shot learning. Extensive experiments demonstrate that it consistently surpasses existing methods of the above three fields by a significant margin.