To detect the rain regions, two types of approaches, one based on reconstruction error comparison (REC) via a learned rain dictionary and the other based on a deep convolutional neural network (DCNN), are presented. With the rain regions, ...
Number of convolutional layers in a CNN Number of LSTM units in an RNN Choice of optimizer Learning rate Regularization Data augmentation Data preprocessing Batch size If you try to change too many of the above variables at once, you’ll lose track of which changes really had the most impact...
In contrast to sparse coding and shallow neural net- works, deep learning has recently been applied to RGB based hyperspectral reconstruction. Galliani et al. [30] first introduced a convolutional neural network for spec- tral reconstruction from a single RGB image. They adapted the Tiramisu ...
In particular, image processing has been largely impacted by convolutional neural networks (CN... VAD Oliveira,T Oberlin,M Chabert,... - International Workshop on On-board Payload Data Compression 被引量: 0发表: 2020年 加载更多来源会议 2024 IEEE International Conference on Visual Communications ...
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012) Google Scholar Kung, H., McDanel, B., Teerapittayanon, S.: NNU Source Repository.https://gitlab.co...
The network used in DeepId consists of four convolutional layers, each followed by a max pooling layer. On top of this lies the fully connected layer which is referred to as DeepId layer. The layer was named so because the DeepIds are extracted from this layer. DeepId layer is then followe...
Since z is low-dimensional, CNNs with reduced mean layers and 1 × 1 convolutional layers can guarantee the flexibility dimension of the input y. To input the ground-truth data x and y into h θ ( · ) , we use the forward map A and concatenate A ( x ) with y along the channel...
Since z is low-dimensional, CNNs with reduced mean layers and 1×1 convolutional layers can guarantee the flexibility dimension of the input y. To input the ground-truth data x and y into ℎ𝜃(·), we use the forward map 𝒜 and concatenate 𝒜(𝑥) with y along the channel ...
The keypoint detector’s loss function ℒ𝑝 was a fully convolutional cross-entropy loss, and the specific procedure can be found in [37]. Considering that the embedding regions centered at each keypoint should be sifted, the operation can be regarded as the following formulation, which can...