Questions tagged [convolution]

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

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What does it mean when the shape of input images is (600,64,64,3)?

While attempting an assignment, I found that shape of the input image was (600,64,64,3). I thought 3 stood for the number of channels but it's listed as the 4th dimension. What does this mean? This is ...
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CNN Multi-Class Model Only Predicts 1 class for all test images

I am trying to build a CNN model to predict 42 classes. I used pre-trained models for this. I used Xception. This is how I have imported my dataset: ...
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Training a CNN on a large dataset

I am currently trying to build a CNN for around 100,000 images. There are 42 classes. I have used the default batch size of 32. This is how my model looks like: ...
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Does a CNN think things inside the filter are collocated aka dependent on each other?

I am running a 1D CNN on tabular data. The rows are data that I have are not sequential, that is to say they are not part of a time series or ordered string, which is why I am not using an LSTM. So ...
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What is an optimal local sparse structure of a convolutional vision network?

I was reading the InceptionNet Paper, where I found quite a few references to developing a sparse network structure, but I am not clear on what this means. An ...
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Image classification using cnn [closed]

I did image classification using CNN and it successfully classified the images but How to save predicted images to separate folder for example i have two classes cat and dog after prediction how to ...
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What's the best approach to classify labeled stockchart images , based on their curve

I'm working on an exercise where I have selected a lot of different stock chart images, showing the week closing of around 1200 companies at two different periode in time. I have stock charts from ...
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Is there a tool that interactively shows the receptive field of a feature map's element?

The receptive field of an element of a feature map is defined as the elements (and/or pixels) in the previous layers that define that element. I am looking for a tool that would allow me to select any ...
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How to make sense of the input and output for a speech-generation model like WaveNet?

I am currently studying this model speech generation known as WaveNet model by Google. https://arxiv.org/pdf/1609.03499.pdf using the linked original paper and this implementation. I find the model ...
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Custom convolution kernels for dot-product between features in a feature map

I am trying to implement Interaction and Aggregation block from this paper: paper, and I faced difficulties with SIA Module. As I understand the paper, I need to: get KxK patch for each (h, w) ...
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Why does this model print Incompatible dilated conv1d layers?

I was trying to see the layers used in a Wavenet model for speech generation and I can't seem to make sense of the output layers printed by the TF model. Model is this: https://github.com/Rayhane-...
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Extracting the features from several Auto Encoders

I am trying to extract the features of sparsed 3D pictures via a Convolutional Auto Encoder(CAE). Dou to the high computational costs I can not train the CAE over all the samples. I prepared couple of ...
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CNN model contains several images that are null

I'm using a deep CNN with the ReLU activation function. When visualizing the layers (each with 32 filters), several of the filtered images are zeros. I am trying to reason why this may be happening? ...
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25 views

best NN architecture for point prediction

I'm training to predict a single value y (continuos in [0,1]) based on a number of variables ...
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deep learning model gives same probabilities for the same class

I'm building a CNN neural network with Pytorch. Although the model accuracy is 85.6%, after getting image probabilities with torch.exp(output) and getting the top ...
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CNN Architecture comparison standards

I want to add comparison of accuracy section in my study report on CNN Architecture for a medical data. I have already added the comparison by VGG 16, AlexNet etc. Is it a standard to compare the ...
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Caps_Net. searching for example and library to use

Which library is most recommended and easy to use?
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CNN - multidimensional matrix as input or parallel input for parallel CNN

I want to run CNN on 20 channels of images. One way is to run on a 20-channel multidimensional matrix (like RGB ). Another way is to run 20 CNN on one channel at a time ( R apart from G separately ...
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How many computations in a CNN

I have not been able to find an answer, so if it is out there, please let me know. I would like to calculate the amount of time, that a uC needs to give me an Output of an CNN. Therefore, I would ...
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Should I apply 1D or 2D CNN on binary text classification?

I am trying to train a text classification model. For all sentence examples, I limit them up to 32 words, and if there are not exist 32 words, I am creating zero pad arrays. To convert each word to ...
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How to update filter weights in CNN by example?

I'm trying to understand how to update filters weights during backpropagation in CNN. Architecture: RGB Input image 5x5 Convolution (Stride = 1, no zero padding, filter size = 2x2 : [{1, -1},{0,0}]) ...
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using maxpool before 1x1 conv in inception module

there is a model described here (fig 3 page 52, also used in CNN course by Andrew ng) which used to build an inception model. for reducing the cost of convolutional layers, it used a 1x1 conv layer ...
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Implementing the SVHN CNN architecture in Srivastava et al. 2014 Dropout paper

I am trying the implement the CNN architecture introduced in Srivastava et al. 2014 Dropout paper (appendix B.2), for the SVHN dataset. I implemented only the convolutional layers part, without ...
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What is dropout in convolutional layers and how does that different from max-pooling-dropout?

When dropout is applied to fully connected layers some nodes will be randomly set to 0. It is unclear to me how dropout work with convolutional layers. If dropout is applied before the convolutions, ...
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How to determine the configuration of Conv2D layers?

I have this autoencoder with two parts:- encoder and decoder. However, I am having problem defining the configuration of these Conv2D layers. This is how my model look like:- input_img = Input(shape=(...
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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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1answer
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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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1answer
29 views

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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1answer
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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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1answer
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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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1answer
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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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63 views

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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1answer
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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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70 views

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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2answers
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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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