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How to handle rectangular images in convolutional neural networks . . . I think the squared image is more a choice for simplicity There are two types of convolutional neural networks Traditional CNNs: CNNs that have fully connected layers at the end, and fully convolutional networks (FCNs): they are only made of convolutional layers (and subsampling and upsampling layers), so they do not contain fully connected layers With traditional CNNs, the inputs always need
machine learning - What is a fully convolution network? - Artificial . . . Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations Equivalently, an FCN is a CNN without fully connected layers Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
What is a cascaded convolutional neural network? 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) This combination requires the introduction of a new input feature which fulfills the "cascade manner" and
CCNA v7. 0 Exam Answers - Full Labs, Assignments Cisco CCNA v7 Exam Answers full Questions Activities from netacad with CCNA1 v7 0 (ITN), CCNA2 v7 0 (SRWE), CCNA3 v7 02 (ENSA) 2024 2025 version 7 02
What are the features get from a feature extraction using a CNN? So, the convolutional layers reduce the input to get only the more relevant features from the image, and then the fully connected layer classify the image using those features, isn't it? I think I've just understood how a CNN works
Reduce receptive field size of CNN while keeping its capacity? One way to keep the capacity while reducing the receptive field size is to add 1x1 conv layers instead of 3x3 (I did so within the DenseBlocks, there the first layer is a 3x3 conv and now followed by 4 times a 1x1 conv layer instead of the original 3x3 convs (which increase the receptive field)) In doing that, the number of parameters can be kept at a similar level While 1x1 convolutions are
Extract features with CNN and pass as sequence to RNN 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
How do CNNs handle inputs of different sizes and shapes? Also, CNNs have a fixed input shape (Correct me if I am wrong) or at least the input should be of the multiples of the CNN's intended input shape (e g , 112 * 112, 224 * 224, etc) (Although the situation that this may work is rare ) According to this PyTorch page, ResNet (for example) accepts images of any size as long as they are bigger than 224
When training a CNN, what are the hyperparameters to tune first? 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