FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

Xin-Cheng, Wen,; Ye, JiaXin; Luo, Yan; Xu, Yong; Wang, Xuan-Ze; Wu, Chang-Li; Liu, Kun-Hong

doi:10.24963/ijcai.2022/317

Cited by 8 publications

(10 citation statements)

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“…(3) COX2 [35] is a dataset of molecular structures including 467 cyclooxygenase-2 inhibitors. (4) ENZYMES [46] is a dataset of 600 enzymes collected from the BRENDA enzyme database. ( 5) BZR [35] is a collection of 405 ligands for benzodiazepine receptor.…”

Section: Methodsmentioning

confidence: 99%

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Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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Graphs can model complex relationships between objects, enabling a myriad of Web applications such as online page/article classification and social recommendation. While graph neural networks (GNNs) have emerged as a powerful tool for graph representation learning, in an end-to-end supervised setting, their performance heavily relies on a large amount of task-specific supervision. To reduce labeling requirement, the "pre-train, fine-tune" and "pre-train, prompt" paradigms have become increasingly common. In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner. However, existing study of prompting on graphs is still limited, lacking a universal treatment to appeal to different downstream tasks. In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs. GraphPrompt not only unifies pre-training and downstream tasks into a common task template, but also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model in a task-specific manner. Finally, we conduct extensive experiments on five public datasets to evaluate and analyze GraphPrompt. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Data mining.

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Section: Methodsmentioning

confidence: 99%

“…(4) ENZYMES [46] is a dataset of 600 enzymes collected from the BRENDA enzyme database. These enzymes are labeled into 6 categories according to their top-level EC enzyme.…”

Section: B Further Descriptions Of Datasetsmentioning

confidence: 99%

Geography of Technology Transfer in China

Liu

2023

East China Normal University Scientific Reports

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“…In other experiments, for the sake of simplicity and generality, we deploy GCN as the encoder for all the baselines and our proposed framework. [11,35,44], more recently, many studies [7,45,57] have shown that the performance of GNNs will severely degrade when the number of labeled node is limited, i.e., the few-shot node classification problem. Inspired by how humans transfer previously learned knowledge to new tasks, researchers propose to adopt the meta-learning paradigm [9] to deal with this label shortage issue [43].…”

Section: Choice Of Encodermentioning

confidence: 99%

“…MetaTNE [22] and RALE [24] also use episodic metalearning to enhance the adaptability of the learned GNN encoder and achieve similar results. However, those existing works usually directly apply meta-learning to graphs [41], ignoring the crucial distinction from images that nodes in a graph are not i.i.d. data, thus leading to several drawbacks as discussed in the paper.…”

Section: Choice Of Encodermentioning

confidence: 99%

Few-shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

Wang

Liu

et al. 2021

Proceedings of the 29th ACM International Conference on Multimedia

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Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications, whereas the data of rare fine-grained categories is very limited. Therefore, we propose the few-shot finegrained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class. Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling finegrained actions: the inability to capture subtle action details and the inadequacy in learning from data with low inter-class variance. To tackle the first issue, a human vision inspired bidirectional attention module (BAM) is proposed. Combining top-down task-driven signals with bottom-up salient stimuli, BAM captures subtle action details by accurately highlighting informative spatio-temporal regions. To address the second issue, we introduce contrastive metalearning (CML). Compared with the widely adopted ProtoNet-based method, CML generates more discriminative video representations for low inter-class variance data, since it makes full use of potential contrastive pairs in each training episode. Furthermore, to fairly compare different models, we establish specific benchmark protocols on two large-scale fine-grained action recognition datasets. Extensive experiments show that our method consistently achieves state-of-the-art performance across evaluated tasks. CCS CONCEPTS• Computing methodologies → Activity recognition and understanding.

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“…Recently, many few-shot learning frameworks [15,45,53,56] have been proposed to deal with new tasks with limited samples. Typically, the main idea is to learn meta-knowledge from base classes with abundant samples (e.g., photo classes such as portraits).…”

Section: Introductionmentioning

confidence: 99%

Federated Few-shot Learning

Wang

Ding

et al. 2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients may only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines. Our code is provided 1 . CCS CONCEPTS• Computing methodologies → Distributed algorithms.

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FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs

Cited by 8 publications

References 0 publications

Geography of Technology Transfer in China

Geography of Technology Transfer in China

Few-shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

Federated Few-shot Learning

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