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A Study of incremental Learning model using deep neural network

Journal: International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) (Vol.10, No. 2)

Publication Date:

Authors : ;

Page : 658-663

Keywords : :Natural language processing (NLP); Comma-separated values(CSV); Convolution neural network (CNN); Stochastic gradient descent(SGD); Adaptive Moment Estimation(ADAM); Canadian Institute For Advanced Research(CIFAR-10); MNIST;

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Abstract

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.

Last modified: 2021-04-10 16:10:14