While classifier-level approaches include thresholding, modified cost function by assigning a higher weight to the minority class misclassification, different cost functions in CNN learning (e.g., pixel-wise cross entropy loss, dice loss), one-class classification and ensemble approaches. The ...
So in short we can say that Mask RCNN combines the two networks — Faster RCNN and FCN in one mega architecture. The loss function for the model is the total loss in doing classification, generating bounding box and generating the mask. Mask RCNN has a couple of additional improvement...
Encryption test image results: (a) Test image 1, (b) Row-wise shuffled image 1, (c) Column-wise shuffled image 1, (d) Selective shuffled image 1, (e) final diffused image 1. (f) Test image 2, (g) Row-wise shuffled image 2, (h) Column-wise shuffled image 2, (i) Selective ...
σ is the sigmoid non-linearity, k is the number of the layer, ⊙ is the element-wise product and ∗ is the convolution operator. The other major improvement of this model is using CNN to use the available receptive field completely by using stacks. The receptive field in PixelCNN is bo...
In the last two decades, numerous pixel-wise machine-learning HSI classification methods have been studied including support vector machines (SVM) [25], multinomial logistic regression [26,27], boosted decision trees (DTs) [28], and neural networks [29,30,31] with varying degrees of success ...