Yuchen Guo, Guiguang Ding, Jungong Han, and Sheng Tang. Zero-shot learning with attribute selection. Entropy, 20(40):60, 2018.Y. Guo, G. Ding, J. Han, and S. Tang. Zero-shot learning with attribute selection. In AAAI, 2018....
Zero-Shot Learning with Attribute Selection 来自 掌桥科研 喜欢 0 阅读量: 8 作者:Y Guo,G Ding,J Han,S Tang 摘要: Zero-shot learning (ZSL) is regarded as an effective way to construct classification models for target classes which have no labeled samples available. The basic framework is to...
AS:Yuchen Guo, Guiguang Ding, Jungong Han, Sheng Tang. "Zero-Shot Learning With Attribute Selection." AAAI (2018).[pdf] DSSC:Yan Li, Zhen Jia, Junge Zhang, Kaiqi Huang, Tieniu Tan."Deep Semantic Structural Constraints for Zero-Shot Learning." AAAI (2018).[pdf] ...
Yts ⊂ Y and in generalized zero-shot learning setting, the test im- age can be assigned either to seen or unseen classes, i.e. Ytr+ts ⊂ Y with the highest compatibility score. 3.1. Learning Linear Compatibility Attribute Label Embedding (ALE) [3], Deep Visual Se- mantic Embedding ...
Zero-shot Cross-lingual Transfer Learning with Multiple Source and Target Languages for Information Extraction: Language Selection and Adversarial Training 来自 arXiv.org 喜欢 0 阅读量: 3 作者:NT Ngo,TH Nguyen 摘要: The majority of previous researches addressing multi-lingual IE are limited to zero...
Overall, we illustrate 4 papers including an attribute dataset, namely, [1] Zero-shot learning posed as a missing data problem, ICCVW 2017 [2] MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning, ICML 2018 ...
From generalized zero-shot learning to long-tail with class descriptors Dvir Samuel1 Yuval Atzmon2 Gal Chechik1,2 1Bar-Ilan University, Ramat Gan, Israel 2NVIDIA Research, Tel Aviv, Israel dvirsamuel@gmail.com, yatzmon@nvidia.com, gal.chechik@biu.ac.il ...
In this scenario the cost of adding a new object to the test set is dramatically lower than with traditional learning. In fact, it breaks down to adding a row (the attribute-based object description) to matrix K. This is a very low cost as compared to collecting haptic data for the new...
Core attribute learning: To solve the zero-day ransomware detection problem, core attributes need to be extracted from seen classes and correlated between seen and unseen classes to identify ransomware in unseen classes. To this end, the self-coding technique is adopted to encode the original featu...
This learning scenario can be treated as zero-shot learn- ing with additional noisy web training data for unseen cat- egories, or learning from web data with additional well- labeled data from auxiliary categories. In this learning scenario, we develop our framework as illustrated in Figure 1,...