This paper may bring to you a new perspective. Researchers from Microsoft Research Asia have looked into local attention and dynamic depth-wise convolution and found that a common convolution structure is in fact no worse than Transformer. The related paper, “On...
On the Connection between Local Attention and Dynamic Depth-wise Convolution (ICLR 2022 spotlight) arxiv This is the official PyTorch implementation of our paper. We simply replace local self attention by (dynamic) depth-wise convolution with lower computational cost. The performance is on par with...
知识蒸馏将教师模型学习到的知识转移到学生模型从而来提高性能; 轻量级网络设计通过设计一些轻量级操作(如depth-wise convolution)来构建新的网络。 输入分辨率是影响CNN计算量和性能的重要因素。对于同一网络,更高的分辨率通常会导致更大的FLOPs...
网络剪枝 旨在通过一定的标准剪枝对模型性能不敏感的滤波器进行剪枝;低比特量化 指用低比特值来量化权重参数和激活值;知识蒸馏 将教师模型学习到的知识转移到学生模型从而来提高性能;轻量级网络设计 通过设计一些轻量级操作(如depth-wise convolution)来构建新的网络。 输入分辨率是影响CNN计算量和性能的重要因素。对于...
Besides, the self-attention mechanism of the Transformer can effectively extract the global information of sound source spatial distribution from the FB map for improving the localization accuracy, and the depth-wise convolution can reduce the network model parameters and accelerate the training process....
We compute guidance features from input ones using a depth-wise convolution layer 相关代码: classDDFFunction(Function):@staticmethoddefforward(ctx,features,channel_filter,spatial_filter,kernel_size=3,dilation=1,stride=1,head=1,kernel_combine='mul',version=''):# check argsassertfeatures.is_cuda,'...
DML_CONVOLUTION_OPERATOR_DESC结构 DML_CREATE_DEVICE_FLAGS 枚举 DML_CUMULATIVE_PRODUCT_OPERATOR_DESC 结构 DML_CUMULATIVE_SUMMATION_OPERATOR_DESC 结构 DML_DEPTH_SPACE_ORDER 枚举 DML_DEPTH_TO_SPACE_OPERATOR_DESC 结构 DML_DEPTH_TO_SPACE1_OPERATOR_DESC 结构 DML_DIAGONAL_MATRIX_OPERATOR_DESC 结构...
动态卷积(Dynamic Convolution)可以被视作为一种在图像处理过程中引入数据相关性的方法(其他的方法 Involution, Bilateral Fliter, etc.),本篇文章从矩阵分解入手,对传统的动态卷积做改进,提升了其性能的同…
We can further improve the spherical harmonic convolution performance by noticing that the reflection function coefficients are constant for each function (Lambert, Phong, and so on). Therefore, each function's coefficients can be computed once (in a preprocess step) and be reused by a...
the dynamic multi-scale convolution (DMSC), improving merging features under different reception fields, and the dynamic weights and bias generator (DWBG) in classification layers to enhance generalization ability. More importantly, owing to the use of depth-wise convolution, the DDUNet is a light...