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Jun 12, 2020 · 21 I was surveying some literature related to Fully Convolutional Networks and came across the following phrase, A fully convolutional network is achieved by replacing the parameter-rich fully connected layers in standard CNN architectures by convolutional layers with 1×1 1 × 1 kernels I have two questions What is meant by parameter-rich?Why would “CNN-LSTM” be another name for RNN, when it doesn’t even have RNN in it? Can you clarify this? What is your knowledge of RNNs and CNNs? Do you know what an LSTM is?Aug 6, 2019 · A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN) See this answer for more info An example of an FCN is the u-net, which does not use any fully connected layers, but only convolution, downsampling (i e pooling), upsampling (deconvolution), and copy and crop operations Sep 30, 2021 · 0 I’m building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN Mar 8, 2018 · A convolutional neural network (CNN) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer Sep 12, 2020 · But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN And then you do CNN part for 6th frame and you pass the features from 2,3,4,5,6 frames to RNN which is better The task I want to do is autonomous driving using sequences of images Dec 30, 2018 · The concept of CNN itself is that you want to learn features from the spatial domain of the image which is XY dimension So, you cannot change dimensions like you mentioned Mar 30, 2020 · 7 5 2 Module Quiz – Ethernet Switching Answers 1 What will a host on an Ethernet network do if it receives a frame with a unicast destination MAC address that does not match its own MAC address? It will discard the frame It will forward the frame to the next host It will remove the frame from the media It will strip off the data-link frame to check the destination IP address 12 You can use CNN on any data, but it’s recommended to use CNN only on data that have spatial features (It might still work on data that doesn’t have spatial features, see DuttaA’s comment below) A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems CNNs have become the go-to method for solving any image data challenge while RNN is used for ideal for text and speech analysis
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