Sentence examples for convolution operations from inspiring English sources

The chosen face detection method is the well-known Convolutional Face Finder (CFF) algorithm, which consists of a pipeline of convolution operations.

It is observed that the proposed scheme helps to reduce dynamic power consumption in the 2D convolution operations up to 33%.

Convolution operations applied on an equalized and normalized input domain are considered to specify the corresponding fuzzification of a crisp partition.

The proposed architecture design is capable of performing convolution operations for 63.3, 1024×1024 frames or 66.4 million outputs per second with 22×22 kernel in a Xilinx's Virtex 2v2000ff896-4 FPGatat maximum clock frequency of 66.4 MHz.

"This is a promising real-world demonstration of SRAM-based in-memory analog computing for deep-learning applications," says Dario Gil, vice president of artificial intelligence at IBM. "The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays.

We use a correctness-preserving transformation to simplify the output computation: a global transformation of imprecision of inputs, vagueness of antecedent terms and smoothness requirements of outputs into a set of off-line convolution operations applied to the corresponding antecedent crisp partition.

However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced.

where the operator ⊗ denotes the 2D convolution operation, and FLPF x, y) denotes a spatial low-pass filter kernel function and is subject to the condition ∬ F LPF x, y dxdy = 1.

The quality of the approximation in Eq. (14) depends on how well the (sum _{k=0}^{n-1} {mathbf {A}}^k) operator is approximated by the convolution operation ({mathbf {C}}_{{mathbf {q}}_n}).

where k i is the i-th basis convolution kernel, α i denotes the i-th weight matrix, ⊗ denotes the convolution operation, ∘ denotes the pixel-wise multiplication operator, and C is the number of basis convolution kernels.

The operator in the expression denotes a truncated linear convolution operation between the two vector sequences and of length and, respectively.