Finally, you can see that the validation loss and the training loss both are in sync. It shows that your model is not overfitting: the validation loss is decreasing and not increasing, and there is rarely any gap between training and validation loss throughout the training phase. Therefore, ...
The loss function of beta-VAE: L=E_{q(z|x)}[log\ p(x|z)]-\beta*KL[q(z|x)||p(z)] 4.5.2 Disentanglement The decreasing KL damages the reconstruction capability. It is not proper to evaluate by the original lower bound. Beta VAE proposed a classifier-based disentanglement metric ...
1. 通过auto-encode网络,输入是正常的图片,输出也是预测图片,与输入图片构成MSE LOSS, 因为网络没有学习到缺陷的样子,因此当缺陷图片输入的时候,预测的结果无法生成有缺陷的位置,因此缺陷图片减去预测图片,结果就是缺陷的位置信息。 Method The proposed network architecture is shown in Figure 1. It is similar to...
We conduct a random search on autoencoders using MSE as the loss function for achieving good reconstruction from the latent space. In these experiments, we consider SNR to verify that minimizing MSE is a meaningful objective when tuning autoencoder hyperparameters. Here, we are not searching for...
since Cifar10 have only 50k training data, we increase the pretraining epoch from 400 to 2000, and the warmup epoch from 40 to 200. We noticed that, the loss is still decreasing after 2000 epoches. we decrease the batch size for training the classifier from 1024 to 128 to mitigate the...
1. 通过auto-encode网络,输入是正常的图片,输出也是预测图片,与输入图片构成MSE LOSS, 因为网络没有学习到缺陷的样子,因此当缺陷图片输入的时候,预测的结果无法生成有缺陷的位置,因此缺陷图片减去预测图片,结果就是缺陷的位置信息。 Method The proposed network architecture is shown in Figure 1. It is similar to...
During compression, the information loss across non-significant regions in medical images may be considered negligible, as it generally does not affect the final diagnosis of a disease by a health specialist. As a rule, some of the useful lossless techniques are being discussed here. To access ...
correctness of a crucial and cheap piece of a plane, a high value of FAR would imply to discard a correct piece (just a little economic loss); however, a high value of IPR would imply to accept a crucial piece that is not correct, and to put this piece on a plane with customers. ...
ypredTrain = cellfun(@(x)predict(scatLSTMAutoencoder,x),trainscatFeatures,UniformOutput=false); maeTrain = cellfun(@(x,y)maeLoss(x,y),ypredTrain,trainscatFeatures); ypredValidation = cellfun(@(x)predict(scatLSTMAutoencoder,x),validationscatFeatures,UniformOutput=false); maeValid = cellf...
(\alpha\)is set to 0.001 in all experiments. In addition, it is observed that overall performance of classifiers is improving with decreasing value of\(\alpha\). It indicates that latent vector representation becomes better when priority is given to latent loss component of total loss as ...