Questions tagged [convolution]

For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.

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Efficient scanning method on images/volumes when applying neural network

I am a newbie in neural network. I am using this for one of my physics problems. So, please forgive my sheer lack of knowledge in this field. My neural network is a convolutional neural network with ...
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Transposed Convolution without using Python built-in functions

Amateur here: How can we write a 2D transposed convolution (aka deconvolution) using the steepest descent method given the following restrictions: cannot use any Python built-in functions cannot ...
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Creating a sub-model from pre-trained model

I have a pre-trained model having the following architecture: ...
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Combining convolution operations

Reading an article about 1x1 convolution, I found this: It should be noted that a two step convolution operation can always be combined into one, but in this case [GoogLeNet] and in most other deep ...
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In “Attention Is All You Need”, why are the FFNs in (2) the same as two convolutions with kernel size 1?

In addition, why do we need a FFN in each layer when we already have attention? Here's a screenshot of the relevant section from Vaswani et al. (2017):
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What does “full connection table” mean in Yan LeCuns comment on 1x1 convolutions?

What does "full connection table" mean in Yan LeCuns comment on 1x1 convolutions? In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with ...
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In CNN, how the weights are retained for filters for a particular class [closed]

I am new to CNN, What I have learned so far about the filters is that when we are giving a training example to our model, our model updates the weights by gradient descent to minimize the loss ...
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Convolutional neural network block notation

The paper by He et al. "Deep Residual Learning for Image Recognition" illustrates their residual network in Figure 3 as follows: I am not a neural network expert, so could somebody please explain to ...
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Convolution v.s. Cross-Correlation

I understand that from mathematical point of view, only difference between Convolution and Cross-Correlation is that Convolution is commutative, while Cross-Correlation is now. In many articles ...
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Skip Connections in Residual Modules

I am a beginner in CNN theory and would like to understand the usage of residual modules better. As far as I understand residual modules can be skipped, only the activation function must be computed ...
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Relationship Between Anchors and the Kernel in Faster R-CNN

In the Faster R-CNN paper, namely for the RPN section, it is mentioned that a 3x3 kernel is run over the input feature map to create a 256-channel Conv layer, which is then convolved by a 1x1 kernel ...
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i have trained a model using fer2013 dataset using CNN for Emotion detection. Now i want to use it in a image

I have a trained model and saved the weights in fer.h5. Now i want to use the pre trained model in another set of images and save it to a excel file ...
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Segmentation Network produces noisy output

I've implemented a SegNet and SegNet ReLU variant in PyTorch. I'm using it as a proof-of-concept for now, but what really bothers me is the noise produced by the network. With ADAM I seem to get ...
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How can I inverse a transposed convolutional layer?

I am interested in inversing a transposed convolution operation (or "deconvolution", and not straightforward convolution). A transposed convolution usually maps data points from a smaller feature ...
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What are features in computer vision?

I'm learning how U-NET network works to do semantic segmentation. I think I have understood everything but features. What are those image features? I read that convolutional layers extract features ...
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Fusing batch normalization with deconvolution in neural networks

I am trying to raise the performance of my convolutional neural network and for that reason I am trying to implement batch normalization fusing. Things are fine when I use fuse with convolution layer,...
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How to select checkpoint for model evaluation?

I have trained a deep convolutional neural network for image similarity classification. The network returns whether the images are the same or different. I trained the network for 20 epochs and save ...
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Patch wise training vs Full Convolutional Training in semantic segmentation

As mentioned in the title, what are those 2 methods? I already checked this question: Patchwise and Full training, (and the mentioned paper) but i can't really understand the meaning and the process ...
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How to understand the weights and biases for beginners?

I am newbie to deep learning, I was building my first model using MNIST dataset, I understood the full model, but one thing is a bit confusing to me. How can we get the weights and bias? Is it that, ...
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Efficient implementation of seperable convolution in tensorflow

It seems like the native implementation of separable convolution in tensorflow is not efficient. https://github.com/tensorflow/tensorflow/issues/12940 Is anyone aware how can we get an efficient ...
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Manual way to draw accuracy/loss graphs

During the training process of the convolutional neural network, the network outputs the training/validation accuracy/loss after each epoch as shown below: ...
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What's the purpose of padding with Maxpooling?

As mentioned in the question, i've noticed that sometimes there are pooling layers with padding. More specifically, I found this Keras tutorial, where there's a net which contains ...
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Should the weights be rotated when using SciPy full convolution?

I use SciPy's single.convolve2d in "full" mode to compute gradient w.r.t to convolution layer inputs. In my current implementation, I don't rotate filters as suggested by this article because I assume ...
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Convolution layer dimensions in deeper layers?

I am trying to understand the CNN network dimensions: ...
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Text detection on English and Chinese languages

https://arxiv.org/abs/1910.07954 In this paper, we have a convolutional character neural network where we have object detection by taking a character as a basic unit. First, we do character detection ...
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Is there a difference between tf.nn.conv1d and tf.nn.convolution in Tensorflow?

I want to know what is the difference between a these two. For me, they are the same function, so I do not see the reason of existance two same functions.
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How are the Convolution kernels learned?

As I went through the basics of machine learning, I failed to understand how do the Convolutional layers in a CNN learn the convolution kernels. After looking at first few tutorials, I thought the ...
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Implementing “full convolution” to find gradient w.r.t the convolution layer inputs

I've been trying to implement "full convolution" w.r.t to convolution layer inputs. According to this article, it looks like this: So, I wrote this function: ...
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Determining position of anchor boxes in original image using downsampled feature map

From what I have read, I understand that methods used in faster-RCNN and SSD involve generating a set of anchor boxes. We first downsample the training image using a CNN and for every pixel in the ...
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How to use MNIST dataset to make predictions on similar images (colorblindness charts)?

I am trying to use the MNIST dataset to train a convolutional neural network to classify digits written in colorblindness charts. As some people have suggested, I have tried playing with the ...
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Need of maxpooling layer in CNN and confusion regarding output size & number of parameters

In my CNN architecture for binary classification, I have 2 convolutional layers, 2 maxpooling layers, 2 batchnormalization operations, 1 RELu and 1 fullyconnected layer. Case1: When the number of ...
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Backpropagation through the inputs of convolutional layer in LeNet

I am trying to understand backprop for LeNet according to the original article http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf I think I have successfully done backdrop till the C3 ...
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Building a deep learning model to predict 2d arrays

Lets assume we have a 2D Testdata Array (arr1, 5k entries, values 0 to 1) like ...
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Val_accuracy (val_acc) very low

We have a data set that is converted from signal data to video. We want to classify these images using convolution. We tried many different methods but val acc is consistently low. Training accuracy ...
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Why RANDOM noise images always predicted as BIRD?

Say I have fine-tuned a 10-classification ResNet18 network on CIFAR-10 and the accuracy on validation set is about 93%. However when feeding into 5000 random noise images (Gaussian noise with the ...
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How to find the various matrix sizes in designing a CNN

I am trying to understand CNN especially the maths and working mechanism using Matlab as the coding language. I have few confusion regarding the concept and the associated programming and will be ...
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How to perform convolution with kernel bigger than image?

In this question I've seen an example of convolution by the kernel with the shape bigger than initial image's one: ...
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Creating deformed convolution using attention mask in Keras

I wanted to create deformable convolution network in Keras and compare its performance with standard convolution in Keras. I tried on MNIST fashion data set. Code for Standard convolution in its ...
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my cifar10 kaggle result around 10% is there something wrong with my pytorch code/?

I'm quite new to pytorch so I want check is there something wrong I got final submission code score around 10% here is my code ...
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Convolutional layers without pooling

I am studying the CNN architecture of the AlexNet, and I have seen that it has convolutional layers without pooling in between: but I don' understand why this is done. Wouldn't be better to have ...
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How do I use Conv2DTranspose to create my decoder?

I am constructing VAE of input 80x60. I have my encoder below but I am troubles making the decoder as it does not conform to 80 x 60. Here is my decoder. Below is the code for constructing the ...
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Why are Convolutional Networks not using cross-correlation

To my knowledge cross-correlation is used to measure the similarity of certain values, like to images. Same applies to the process of feature extraction in CNNs, where input matrices are multiplied by ...
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How does the “skip” method work for upsampling? (fully convolutional NN)

I'm studying fully convolutional neural networks for image segmentation, so far i've study and kind of understood the deconv network. Following this tutorial (Upsampling) i can't really understand how ...
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if two convolution layer connected in tandem follow associative property of convolution?

Two Convolution filter follow the associative property as follows :- I want to ask whether this property will hold for two convolution layer with no operation in between them?
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Capsule networks for binary classification not training brain images classification

Currently I am trying to implement a capsule network using Xifeng Guo's Keras code for capsule nets. I have a dataset of brain tumor images with 98 negatively labeled instances and 155 positively ...
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Unsupervised image classification?

Does this exist? What algorithm or combinations of algorithms would be able to classify images without supervision? For example if you have many pictures of cats and dogs, then without being trained ...
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Different convolutions in CNN

I have a simple question. Why only convolution is used in CNN? There are a lot of possible rules for combining a filter and an image. Why is pixel-wise convolution the standard? For example, dropout ...
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Precision-Recall for CNN place recognition problem

Given 3450 query and 3450 reference images in a place recognition problem, I plot the ...
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How to Visualize Graph Attention

I am quite new to the concept of attention. I am working with graph data and running graph convolution on it to learn node level embedding first. Then an attention layer to aggregate the nodes to ...
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Autoencoders for the compression of time series

I am trying to use autoencoder (simple, convolutional, LSTM) to compress time series. Here are the models I tried. Simple autoencoder: ...

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