Training auto-encoders effectively via eliminating task-irrelevant input variables
نویسندگان
چکیده
منابع مشابه
Training Auto-encoders Effectively via Eliminating Task-irrelevant Input Variables
Auto-encoders are often used as building blocks of deep network classifier to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalization performance of the network. In this paper,via dropping the task-irrelevant input variables the performance of auto-encoders can be obviously improved .Specifically, an importance-bas...
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ژورنال
عنوان ژورنال: International Journal of Computational Science and Engineering
سال: 2019
ISSN: 1742-7185,1742-7193
DOI: 10.1504/ijcse.2019.10020472