Few-shot model agnostic federated learning is a technique used in machine learning that allows multiple parties with different data sets to train a shared model without sharing the data. In traditional federated learning, each party would contribute their data to a central server where the model is...
【论文笔记】Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detecti Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection 基于耦合卷积神经网络的弱监督学习用于飞机检测 Fan Zhang, Bo Du, Senior Member, IEEE, Liangpei Zhang, Sen...
In addition, this paper provides some discussions on open challenges that few/zero-shot learning brought to visual semantic segmentation, such as cross-domain few/zero-shot segmentation and generalized few/zero-shot segmentation. In summary, the main contributions of this paper are as follows: 1)...
Hybrid graph neural networks for few-shot learning Hubs and hyperspheres: Reducing hubness and improving transductive few-shot learning with hyperspherical embeddings Revisiting prototypical network for cross domain few-shot learning Transductive few-shot learning with prototype-based label propagation by it...
最近接触Meta-Learning,肯定得看看2017年发表在ICML的MAML: 《Model-AgnosticMeta-Learningfor Fast Adaptation of DeepNetworks 》,分享一下比较好的学习资料: 对MAML算法及整体框架的理解: 知乎回答:https://zhuanlan.zhihu.com/p/57864886 对于Algorithm1
Figure 1. Federated few-shot learning for machinery fault diagnosis across multiple domains. (1) We proposed a federated few-shot learning-based fault-diagnosis framework, which leverages fault data from multiple source domains to construct a unified few-shot fault-diagnosis model. By doing so, ...
Our method is built under a multi-task learning framework. Each saliency mask is divided into two parts, one of which is used as a support mask in a random way, while the other part is used as a query mask to participate in the model training of few-shot meta learning. To further ...
Our method is built under a multi-task learning framework. Each saliency mask is divided into two parts, one of which is used as a support mask in a random way, while the other part is used as a query mask to participate in the model training of few-shot meta learning. To further ...
The model-agnostic meta-learning (MAML) algorithm is presented as a foundational optimization-based meta-learning method. It has been pivotal in demonstrating how models can be tuned to new tasks efficiently using just a few iterations. Progressing further, we propose the gradient-oriented ...