Self-Progressing Robust Training

using game theory techniques in self-organizing maps training

شبکه خود سازمانده پرکاربردترین شبکه عصبی برای انجام خوشه بندی و کوانتیزه نمودن برداری است. از زمان معرفی این شبکه تاکنون، از این روش در مسائل مختلف در حوزه های گوناگون استفاده و توسعه ها و بهبودهای متعددی برای آن ارائه شده است. شبکه خودسازمانده از تعدادی سلول برای تخمین تابع توزیع الگوهای ورودی در فضای چندبعدی استفاده می کند. احتمال وجود سلول مرده مشکلی اساسی در الگوریتم شبکه خودسازمانده به حسا...

Robust and Reliable Feature Extractor Training by Using Unsupervised Pre-training with Self-Organization Map

Recent research has shown that deep neural network is very powerful for object recognition task. However, training the deep neural network with more than two hidden layers is not easy even now because of regularization problem. To overcome such a regularization problem, some techniques like dropout and de-noising were developed. The philosophy behind de-noising is to extract more robust feature...

Robust Speech Recognition Training

Improved Training for Self-Training

It is well known that for some tasks, labeled data sets may be hard to gather. Self-training, or pseudo-labeling, tackles the problem of having insufficient training data. In the self-training scheme, the classifier is first trained on a limited, labeled dataset, and after that, it is trained on an additional, unlabeled dataset, using its own predictions as labels, provided those predictions ar...

Cross-domain robust acoustic training

This paper describes our efforts towards cross-domain acoustic training for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We used weighted multi-style training by pooling insufficient telephony landline and cellular data with down sampled wide band clean data to develop better hybrid acoustic models. We explored the effects on decision tree size to accuracy by approximately 10...