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I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I r...
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.
May 31, 2021 · The paper here in section 2.1 Coarse-to-fine prediction: To increase the field of view presented to the CNN and reduce the redundancy among neighboring voxels, each image is downsampled by a facto...
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.
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.
May 13, 2019 · 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.
3 The paper you are citing is the paper that introduced the cascaded convolution neural network. In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN).
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 $1 \times 1$ kernels. I have two questions. What is meant by parameter-rich?
In a CNN (such as Google's Inception network), bottleneck layers are added to reduce the number of feature maps (aka channels) in the network, which, otherwise, tend to increase in each layer. This is achieved by using 1x1 convolutions with fewer output channels than input channels.
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