Recent works handle the problem by selecting samples with clean labels based on loss values using a fixed threshold for all the classes which may not always be reliable. They also depend upon the noise rate in the data which may not always be available. In this work, we propose a novel ...
Deep learning Ensemble regression Robust loss functions 1. Introduction For centuries philosophers, artists, and scientists have tried to discover the mystery of beauty [1]. In fact, the beauty of the face is gaining more and more interest due to the rapid development of plastic surgery and cosm...
This practice can also lead to data loss or to LDM database corruption. Server clusters Dynamic disks aren't supported for use with Windows Clustering. This restriction doesn't prevent you from extending an NTFS volume that is contained on a cluster shared disk (a disk that is sh...
Our method ensures robust learning amidst outliers, influenced by tissue deformation, smoke, and surgical instruments, by utilizing a unique loss function. This function adjusts the selection and weighting of depth data for learning based on their given confidence. We trained the model using the ...
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Since each neuron is susceptible to being masked, neurons learn the ability to adapt to the lack of information, thus making the NN more robust and avoid overfitting. We also use batch normalization (Ioffe and Szegedy, 2015) between the activation function and the dropout, to normalize the ...
for training. First, the encoder part was frozen at the beginning of training, and only the decoder part was trained and converged 70 times. Then, the encoder part was unfrozen and trained together with the decoder part. The learning rate during training is attenuated if the loss does not ...
Therefore, we activate the regularization term of the loss function and directly constrain the output of the gate modules. For this stage, the learning rate needs to be made smaller after the skip ratio is stabilized. This can make the DRDN get better denoising ability. Optional. We also ...
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