Figure 4. Examples of two sparse feature maps during iterations. 2.1.3 Image reconstruction After R iterations, the sparse feature maps UR are finally fed into a convolutional layer to generate the output image.
The average pooling layer (convolutional kernel, 2 × 2 × 2; step size, 2 × 2 × 2) allowed to reduce the number of the network parameters, minimize overfitting, and reduce the model’s complexity. On the other hand, the transition layer solved the problem of changing...
The examples of object-part relationship for SOD. For the first row, the tail wing of an aircraft is not recognized as the salient object by FCNs while our proposed method predicts the aircraft as a whole. In the second row, there is an incoherence in the right arm of the cartoon figure...
be solved by a closed-form solution. The gradient propagation is unblocked for the coding layer and the convolutional layer under an end-to-end framework. Overall, the layer is readily pluggable into any CNN architecture and amenable to training via standard backpropagation....
Many different algorithms, based both on classical methods and machine learning techniques, have been tested for the decoding of surface codes. Apart from the MWPM previously introduced, examples of decoding algorithms not based on neural networks are the renormalization group decoder [21,22], the ...
A single GTX 580 GPU has only 3GB of memory, which limits the maximum size of the networks that can be trained on it. It turns out that 1.2 million training examples are enough to train networks which are too big to fit on one GPU. Therefore we spread the net across two GPUs. Curren...
On the other hand, the transition layer solved the problem of changing the number of channels brought by multiple dense blocks in series, unified the size of the feature maps of each layer, and facilitated the skip connection during the up-sampling. The transition layer reduced the image to ...
A single GTX 580 GPU has only 3GB of memory, which limits the maximum size of the networks that can be trained on it. It turns out that 1.2 million training examples are enough to train networks which are too big to fit on one GPU. Therefore we spread the net across two GPUs. Curren...
Such an embedding can be consid- ered as a hashing function on the data, which translates the underlying similarity into the collision probability of the hash or, more generally, into the similarity of the codes under the Hamming metric. Examples of recent similarity-preserving hashing methods ...
Such an embedding can be considered as a hashing function on the data, which translates the underlying similarity into the colli- sion probability of the hash or, more generally, into the similarity of the codes under the Hamming metric. Examples of recent similarity-preserving hashing methods...