Advances and Applications in Deep Learning最新文献

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Transfer Learning and Deep Domain Adaptation 迁移学习与深度域适应

Advances and Applications in Deep Learning Pub Date : 2020-10-29 DOI: 10.5772/intechopen.94072

Wen Xu, Jing He, Yanfeng Shu

引用次数: 20

Deep Learning Enabled Nanophotonics 深度学习支持纳米光子学

Advances and Applications in Deep Learning Pub Date : 2020-07-30 DOI: 10.5772/intechopen.93289

Lujun Huang, Lei Xu, A. Miroshnichenko

引用次数: 14

Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models 可解释人工智能(xAI)方法和深度元学习模型

Advances and Applications in Deep Learning Pub Date : 2020-06-25 DOI: 10.5772/INTECHOPEN.92172

Evren Daglarli

{"title":"Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models","authors":"Evren Daglarli","doi":"10.5772/INTECHOPEN.92172","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.92172","url":null,"abstract":"The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.","PeriodicalId":129871,"journal":{"name":"Advances and Applications in Deep Learning","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121992477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17