Search results with tag "Depthwise"
MobileNetV2: Inverted Residuals and Linear Bottlenecks
openaccess.thecvf.comDepthwise separable convolutions are a drop-in re-placement for standard convolutional layers. Empiri-cally they work almost as well as regular convolutions but only cost: hi ·wi ·di(k 2 +d j) (1) which is the sum of the depthwise and 1 × 1 pointwise convolutions. Effectively depthwise separable convolu-
Xception: Deep Learning With Depthwise Separable …
openaccess.thecvf.comdepthwise separable convolutions in the TensorFlow framework [1]. • Residual connections, introduced by He et al. in [4], which our proposed architecture uses extensively. 3. The Xception architecture We propose a convolutional neural network architecture based entirely on depthwise separable convolution layers.
fchollet@google - arXiv
arxiv.orgDepthwise separable convolutions, which our proposed architecture is entirely based upon. While the use of spa-tially separable convolutions in neural networks has a long history, going back to at least 2012 [12] (but likely even earlier), the depthwise version is more recent. Lau-rent Sifre developed depthwise separable convolutions
Andrew G. Howard Menglong Zhu Bo Chen Dmitry …
arxiv.orgDepthwise separable convolution are made up of two layers: depthwise convolutions and pointwise convolutions. We use depthwise convolutions to apply a single filter per each input channel (input depth). Pointwise convolution, a simple 1 1convolution, is then used to create a …
Encoder-DecoderwithAtrous Separable Convolution for ...
openaccess.thecvf.comDepthwise separable convolution:Depthwiseseparableconvolution[27,28] or group convolution [7,65], a powerful operation to reduce the computation cost and number of parameters while maintaining similar (or slightly better) perfor-mance. This operation has been adopted in many recent neural network designs
Comparison of YOLOv3, YOLOv5s and MobileNet-SSD V2 for ...
www.irjet.netdepthwise separable convolution, which lowered the model size and complexity cost of the network to a decent level, to make it usable for low processing applications. Thereafter in the second edition of the MobileNet family, an inverted residual structure is provided for much better modularity and this version has been named MobileNetV2.
Neural Architecture Search: A Survey
www.jmlr.orgoperations like depthwise separable convolutions (Chollet, 2016) or dilated convolutions (Yu and Koltun, 2016); and (iii) hyperparameters associated with the operation, e.g., number of lters, kernel size and strides for a convolutional layer (Baker et al., 2017a; Suganuma
Understanding and Simplifying One-Shot Architecture Search
proceedings.mlr.presslearning has been used to optimize other components of ... tions, a pair of 5x5 convolutions, a max pooling layer, or an identity operation. However, only the 5x5 convolutions’ ... depthwise separable 3x3 convolutions, (3) a pair of depth-+ Understanding and Simplifying One-Shot Architecture Search architecture search.
fzhangxiangyu,zxy,linmengxiao,[email protected] arXiv ...
arxiv.orgdepthwise separable convolutions or group convolutions into the building blocks to strike an excellent trade-off between representation capability and computational cost. However, we notice that both designs do not fully take the 1 convolutions (also called pointwise convolutions in [12]) into account, which require considerable complex-ity.