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Attribute-Based Synthetic Network (ABS-Net): Learning more from pseudo feature representations

Jiang Lu, Jin Li, Ziang Yan, Fenghua Mei, Changshui Zhang
2018 Pattern Recognition  
generate pseudo feature representations using a probability based sampling strategy to facilitate subsequent training process of class predictor.  ...  In this paper we present a simple but high-performance ZSL approach by generating pseudo feature representations (GPFR).  ...  It's remarkable that function E is learned via dataset S but P via synthetic pseudo feature representations of unseen classes.  ... 
doi:10.1016/j.patcog.2018.03.006 fatcat:zqo2pjvwsfgmjjrkn5dsrccfya

Generalized Zero Shot Learning via Synthesis Pseudo Features

Chuanlong Li, Xiufen Ye, Haibo Yang, Yatong Han, Xiang Li, Yunpeng Jia
2019 IEEE Access  
Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes.  ...  The first one is the synthesis strategy; the proposed strategy directly synthesizes the pseudo features of unseen classes contrary to current synthesis-based methods, which synthesize pseudo instances.  ...  ABS-NET [12] first learns credible feature representations for each attribute by utilizing a prepared dataset of seen classes.  ... 
doi:10.1109/access.2019.2925093 fatcat:3fb5ymjmuzd43p55oguxndm42a

Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

Manish Kumar Tripathi, Abhigyan Nath, Tej P. Singh, A. S. Ethayathulla, Punit Kaur
2021 Molecular diversity  
The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space  ...  As different attributes/features of the data present a different facet, therefore, it is not known in prior as to which feature /attribute will result in better training for a machine learning algorithm  ...  These higher-order representations can be used as features/attributes in the training of learning algorithms. Autoencoders are mainly used for dimensionality reduction and anomaly detection [119] .  ... 
doi:10.1007/s11030-021-10256-w pmid:34159484 pmcid:PMC8219515 fatcat:p3lsp57x6rbnxgxdu7y5dggdeu

Learning from Very Few Samples: A Survey [article]

Jiang Lu, Pinghua Gong, Jieping Ye, Changshui Zhang
2020 arXiv   pre-print
emphasize particularly on the meta learning based FSL approaches.  ...  The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their  ...  ACKNOWLEDGMENTS The authors would like to thank the pioneer researchers in few sample learning and other related fields.  ... 
arXiv:2009.02653v2 fatcat:fytfbeifmnhbfodat6czwxsqeu