联邦持续学习(federated continual learning)中存在多个客户端, 每个客户端不断获取新的数据. 由于存储空间的限制, 客户端以持续学习的方式进行模型更新, 同时客户端之间通过共享知识提升本地模型性能. Yoon等 [9]最早给出了联邦持续学习的定义, 其提出的方法将模型参数拆分为通用参数和任务特定参数, 客户端之间共享通...
The features are reconstructed using basis vectors in sub- prototype space, and since all majority and minority classes share these vectors, the rich knowledge of the majority classes can assist the minority classes in learning a more robust representation. Duri...
{Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast}, author={Zhu, Boqing and Xu, Kele and Wang, Changjian and Qin, Zheng and Sun, Tao and Wang, Huaimin and Peng, Yuxing}, booktitle={Proceedings of the Thirty-First International Joint Conference on Artificial ...
Tom Gilray suggests using a simplified intermediate representation (IR) that disallows shadowing, hasifbut notcond, etc. Could have the IR be the macro expanded code. Could possibly reverse engineer/infer macro calls that could have produced the IR. ...
To give an intuition of open set recognition problem, Fig.1shows the difference between closed set recognition and open set recognition from the perspective of representation learning. Assuming that a data set contains four known categories, and with the open set condition, the data set might also...
We propose a local approach, focusing on explaining predictions for a specific sample, and is mainly divided into three parts; tree-filtering, low dimensional representation, and prototype ruleset extraction. Here, we employ RSF as the ensemble model but our approach is generalised to other ...
Under this representation method, identity is regarded as a special form of extension name; that is, when the content name is empty, so we use the prefix tree as a data structure to support the storage and query operations of names and identities as shown in Fig. 5.31. Fig. 5.31 Multiple...
The superior performance of DNNs stems from their ability to extract high-level features from raw data by performing statistical learning on large amounts of data to obtain an efficient representation of the input space7,8,9. This is quite different from earlier machine learning approaches that ...
The superior performance of DNNs stems from their ability to extract high-level features from raw data by performing statistical learning on large amounts of data to obtain an efficient representation of the input space7,8,9. This is quite different from earlier machine learning approaches that ...
To enhance the expression ability of distributional word representation learning model, many researchers tend to induce word senses through clustering, and learn multiple embedding vectors for each word, namely multi-prototype word embedding model. However, most related work ignores the relatedness among ...