Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
Hierarchical graph neural networks for few-shot learning
Recent graph neural network (GNN) based methods for few-shot learning (FSL) represent
the samples of interest as a fully-connected graph and conduct reasoning on the nodes …
the samples of interest as a fully-connected graph and conduct reasoning on the nodes …
Few-shot cross-domain fault diagnosis of bearing driven by task-supervised ANIL
Meta-learning has effectively addressed the limit of deep learning fault diagnosis models
that demands a large number of samples. However, existing meta-learning models lack the …
that demands a large number of samples. However, existing meta-learning models lack the …
Meta attention-generation network for cross-granularity few-shot learning
Fine-grained classification with few labeled samples has urgent needs in practice since fine-
grained samples are more difficult and expensive to collect and annotate. Standard few-shot …
grained samples are more difficult and expensive to collect and annotate. Standard few-shot …
Automatic underwater fish species classification with limited data using few-shot learning
Underwater cameras are widely used to monitor marine biodiversity, and the trend is
increasing due to the availability of cheap action cameras. The main bottleneck of video …
increasing due to the availability of cheap action cameras. The main bottleneck of video …
Subgraph-aware few-shot inductive link prediction via meta-learning
Link prediction for knowledge graphs aims to predict missing connections between entities.
Prevailing methods are limited to a transductive setting and hard to process unseen entities …
Prevailing methods are limited to a transductive setting and hard to process unseen entities …
Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition
With the rapid growth of the Internet of Things (IoT), smart systems and applications are
equipped with an increasing number of wearable sensors and mobile devices. These …
equipped with an increasing number of wearable sensors and mobile devices. These …
Dual-tree genetic programming for few-shot image classification
Few-shot image classification (FSIC) is an important but challenging task due to high
variations across images and a small number of training instances. A learning system often …
variations across images and a small number of training instances. A learning system often …
Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques
Landslide susceptibility assessment (LSA) evaluates the likelihood of landslide occurrences
and can help mitigate and prevent landslide risks. Recently, there have been vast …
and can help mitigate and prevent landslide risks. Recently, there have been vast …
MuL-GRN: Multi-level graph relation network for few-shot node classification
Few-shot learning (FSL) that acquires new knowledge with little supervision, attracts much
attention due to expensive cost of data annotation. Various meta-learning methods have …
attention due to expensive cost of data annotation. Various meta-learning methods have …