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China-JI-JI Κατάλογοι Εταιρεία
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Εταιρικά Νέα :
- What are the features get from a feature extraction using a CNN?
By accessing these high-level features, you essentially have a more compact and meaningful representation of what the image represents (based always on the classes that the CNN has been trained on) By visualizing the activations of these layers we can take a look on what these high-level features look like
- machine learning - What is a fully convolution network? - Artificial . . .
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? Is it called parameter rich because the fully connected layers pass on parameters without any kind of "spatial
- What is the fundamental difference between CNN and RNN?
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
- convolutional neural networks - When to use Multi-class CNN vs. one . . .
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 That is, if I'm making e g a
- Extract features with CNN and pass as sequence to RNN
$\begingroup$ 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
- In a CNN, does each new filter have different weights for each input . . .
Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel So the diagrams showing one set of weights per input channel for each filter are correct
- How to handle rectangular images in convolutional neural networks . . .
Almost all the convolutional neural network architecture I have come across have a square input size of an image, like $32 \\times 32$, $64 \\times 64$ or $128 \\times 128$ Ideally, we might not have a
- 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))
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