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A Study of incremental Learning model using deep neural network
Publication year - 2021
Publication title -
international journal of advanced trends in computer science and engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3091
DOI - 10.30534/ijatcse/2021/281022021
Subject(s) - forgetting , artificial intelligence , computer science , deep learning , process (computing) , field (mathematics) , machine learning , incremental learning , artificial neural network , deep neural networks , mathematics , philosophy , linguistics , pure mathematics , operating system
Deep learning has arrived with a great number of advances in the research of machine learning and its models. Due to the advancements recently in the field of deep learning and its models especially in the fields like NLP and Computer Vision in supervised learning for which we have to pre-definably decide a dataset and train our model completely on it and make predictions but in case if we have any new samples of data on which we want our model to be predicted then we have to completely retrain the model, which is computationally costly therefore to avoid re-training the model, we add the new samples on the previously learnt features from the pre- trained model called Incremental Learning. In the paper we proposed the system to overcome the process of catastrophic forgetting we introduced the concept of building on pre-trained model.

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