@conference {Sabiri2022428,
	title = {Impact of Hyperparameters on the Generative Adversarial Networks Behavior},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2022},
	note = {cited By 0},
	pages = {428-438},
	abstract = {Generative adversarial networks (GANs) have become a full-fledged branch of the most important neural network models for unsupervised machine learning. A multitude of loss functions have been developed to train the GAN discriminators and they all have a common structure: a sum of real and false losses which depend only on the real losses and generated data respectively. A challenge associated with an equally weighted sum of two losses is that the formation can benefit one loss but harm the other, which we show causes instability and mode collapse. In this article, we introduce a new family of discriminant loss functions which adopts a weighted sum of real and false parts. With the use the gradients of the real and false parts of the loss, we can adaptively choose weights to train the discriminator in the sense that benefits the stability of the GAN model. Our method can potentially be applied to any discriminator model with a loss which is a sum of the real and fake parts. Our method consists in adjusting the hyper-parameters appropriately in order to improve the training of the two antagonistic models Experiences validated the effectiveness of our loss functions on image generation tasks, improving the base results by a significant margin on dataset Celebdata. Copyright {\textcopyright} 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Common structures, Computer vision, Deep learning, Generative adversarial networks, Hyper-parameter, Image enhancement, Learning systems, Loss functions, Machine-learning, Network behaviors, Neural network model, Neural-networks, Unsupervised machine learning, Weighted Sum},
	doi = {10.5220/0011115100003179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140905402\&doi=10.5220\%2f0011115100003179\&partnerID=40\&md5=a465ab3cd8d0650f9e310edf8a94d422},
	author = {Sabiri, B. and Asri, B. and Rhanoui, M.}
}
@conference {Sabiri2022418,
	title = {Mechanism of Overfitting Avoidance Techniques for Training Deep Neural Networks},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2022},
	note = {cited By 0},
	pages = {418-427},
	abstract = {The objective of a deep learning neural network is to have a final model that performs well both on the data used to train it and the new data on which the model will be used to make predictions. Overfitting refers to the fact that the predictive model produced by the machine learning algorithm adapts well to the training set. In this case, the predictive model will capture the generalizable correlations and the noise produced by the data and will be able to give very good predictions on the data of the training set, but it will predict badly on the data that it has not yet seen during his learning phase. This paper proposes two techniques among many others to reduce or prevent overfitting. Furthermore, by analyzing dynamics during training, we propose a consensus classification algorithm that avoids overfitting, we investigate the performance of these two types of techniques in convolutional neural network. Early stopping allowing to save the hyper-parameters of a model at the right time. And the dropout making the learning of the model harder allowing to gain up to more than 50\% by decreasing the loss rate of the model. Copyright {\textcopyright} 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Computer vision, Convolution, Convolutional neural network, Convolutional neural networks, Data overfitting, Deep learning, Deep neural networks, Dropout, Early stopping, Forecasting, Learning algorithms, Learning systems, Machine-learning, Max-pooling, Overfitting, Predictive models, Training sets},
	doi = {10.5220/0011114900003179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140895255\&doi=10.5220\%2f0011114900003179\&partnerID=40\&md5=286b687a1791aefc7394085755e790c8},
	author = {Sabiri, B. and Asri, B.E. and Rhanoui, M.}
}
