… labels, the cross-entropy loss can be calculated as our objective … combination of depthwise
separation convolution and dense connection makes more difference in improving a networks…

… The cross-layer combination increased the depth of the model and strengthened the flow …
complete the classification and lost the spatial information, so the time was slightly faster. The …

… magnitude spectrogram of a mixture signal, by considering … separation for output classification.
This article had studied many loss functions and suggests that principle of logit separation …

… One approach we tried was to impose an additional “separation consistency” loss that
ensures that sources separated from a MoM are similar to those separated from the individual …

… mixed input, there could be an infinite number of possible solutions, we introduce a “Separation-Critic”
- a discriminative network … metrics for image separation, 1) the exclusion loss [17, …

… loss of useful spatial information. To address this issue, many 2-D convolutional neural network
… and speed up the classification process, we adopt the depthwise separable convolution (…

… the corresponding loss function is dice coefficient loss. The … classification problem, but also
a binary image segmentation, naturally, the joint loss function can be defined as combination …

… Additionally, the combination of Data Augmentation and Big … ensures the accurate and
efficient separation of the equipment. … Loss functions commonly used in deep learning-based …

… interclass separable features, which ease classifier learning. … network structure composed
of a contrastive loss for learning … for tail classes as convex combination of existing instances. …

… , this paper adopts the joint learning strategy combining softmax loss and contrastive-center …
Full size image We adopt the depthwise separable convolution to reduce the amount of …