Jul 26, 2022 · This paper suggested an improved convolutional neural network (CNN) method (ICNN-BNDOA), which is based on Batch Normalization (BN), Dropout (DO), and Adaptive ...
This paper suggested an improved convolutional neural network (CNN) method (ICNN-BNDOA), which is based on Batch Normalization (BN), Dropout (DO), and Adaptive ...
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Jul 16, 2024 · Batch normalization not only enhances training stability and convergence speed but also improves the generalization and accuracy of CNN models.
Mar 31, 2024 · I have 10 epochs, the learning rate is 0.001 with 32 batch size and ADAM optimizer. I get an accuracy of around 50% and I am not really sure ...
Sep 30, 2024 · From the results, we inferred that batch normalization contributed to an increase in performance and weight optimization speed. There have ...
Jun 30, 2022 · A new Adam optimizer with power-exponential learning rate is proposed to control the iteration direction and step size of CNN method.
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Oct 17, 2017 · Yes, batch size affects Adam optimizer. Common batch sizes 16, 32, and 64 can be used. Results show that there is a sweet spot for batch size, where a model ...
In this work we show that two modern techniques for training CNNs - Batch Normalization and the Adam Optimizer - substantially improve CNN performance and ...
Jan 22, 2024 · This paper investigates different methods that can be used to improve convolutional neural network performance.
2019. TLDR. This work shows that two modern techniques for training CNNs – Batch Normalization and the Adam Optimizer - substantially improve CNN performance ...