Questions tagged [convolution]

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

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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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How CNN applies backpropagation to update its weights and biases?

I understand that the 3 main layers for CNN are convolutional layer, ReLU layer and pooling layer. However, I do not understand how CNN updates its weights and biases using backpropagation. I ...
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Choosing a set of CNNs for paper

There are so many CNNs out there and im trying to do a comparison between some of them in my paper which networks should I use? Resnet, vgg and inception are obvious but I need 3 or 4 others. which ...
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How to design n-dimensional feature descriptor similar as the input image?

I am re-writing the H-Net code in Keras for cross-domain image similarity. The network architecture is described in the attached paper. I wrote the encoder and decoder parts but unable to get similar ...
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what type of classifier to use for a multiclass multilabel problem where the input dimensions are binary

The input dimension are (100,104,1) in shape and each value could be either 1 or 0. This is basically a multilabel multiclass problem where output needs to be mapped to a 104 bit vector. 104 bit ...
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I'm not getting the no of parameters concept in CNN

Hi guys i've attached two images of question please help me on solution. Thank you
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Is Max Pooling and Conv used on anything else but images?

Can you think of any domain of application, other than 2D images, where it could make sense to use max pooling or convolution? Because the ONNX format allows for non 2D inputs. On the operators page (...
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Winograd Convolution

For https://www.intel.ai/winograd-2/ , why use stride = 2 ? Why need to transform input image pixels ? Why this C++ implementation of winograd convolution does not require any input tensors ...
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How can I stack one feature-engineering based model and another one non-feature engineering based model in python?

I have a StackOverflow question answer dataset. ( this is a classification problem ) So , far I have created two different models. Model 1: LightGBM model optimized. Data fed into LightGBM model ...
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SRCNN - the colors disappear from the output

I'm training a custom CNN (built for academic purpose) to perform Super-Resolution. I based my work on this review. The input of the network is a RGB color image, so 3 channels of size image_width x ...
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Forward and backward pass in Conv2D transpose Layer

I’ve several questions regarding the transposed convolution 2d layer. I’ve not been able to find a proper resource explaining the forward and backward pass. What I know (but not for sure) is, that ...
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How to find layer-wise split of non trainable parametes in keras?

I am working on an image classifier using a CNN architecture in keras. I Instantiated a model with several Conv2D layers followed by batch normalization layers and Pooling layers and then a fully ...
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Backpropagation in a convolutional neural network with stride and padding

So i am trying to learn backpropagation of convolutional neural networks. A lot of articles only cover convolutions without a stride and a padding variable, so i decided to try it on my own. For ...
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SRGAN Generator Architecture: Why is it possible to do this elementwise sum?

Consider the first residual block. Its first convolution layer takes in inputs: THe PRELU's output 64 filters(64 outputs) each one being 3*3 with a stride of (1 ; 1) So I think that the output of ...
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Do CNN convolution and pooling layers get backpropogated?

I can't find a simple answer to this by Googling which leads me to think the answer is no, but I want to be sure... In a feed forward network, all of the layers of weights get backpropogated, but ...
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How to implement N-Dimensional Convolution in TensorFlow / Keras? (with N > 3)

I had an idea for a model that requires multidimensional convolution (i.e. 4D or more). However, I can't find anything higher than 3D Conv on the TF 2.0 / Keras module. Is there a way to implement ...
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Unable to understand the meaning of following lines of the research paper for image segmentation

I am implementing a paper on image segmentation. It is based on the slight modification of the u-net architecture. The paper is based on encoder and decoder steps Following are the lines of the paper ...
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What is the benefit of using Max pooling in convnets as opposed to just using convolution layers? (from Francois Chollet's Deep Learning with Python)

I am reading Francois Chollet's Deep learning with python, and I came across a section about max pooling that's really giving me trouble. I am unable to copy paste the content, so I've included ...
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SRCNN - how to get a color image output training only with luminance

I'm quite new to convolutional neural network, applied to super-resolution. I read this review article, itself based on this paper as an attempt to understand it better. In the review, the author ...
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Does a max-pooling layer in a ConvNet contribute to the “vanishing gradient” problem?

I would answer no, but am not sure if I'm missing something and hope you can help me out: The derivative of a max-pooling layer in a ConvNet is one w.r.t. the maximum value and zero for all others. A ...
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What is the difference between multiply and dot functions that is used to merge layer in Keras?

I want to merge two CNN deep learning model using Keras and would like to know what is the difference multiply and dot functions that is used to merge layer? ...
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Atrous convolution allows arbitrary feature map resolution

I'm reading Deeplab paper. In this paper, the authors proposed to use atrous convolution, whose 1-D form is: $\hspace{3.0cm} y[i] = \sum_k x[i + r \cdot k] w[k]$ Given this scheme, they wrote that ...
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Saving images in a non-retraceable way, but still able to train R-CNN's on them

For a computer vision project I am working with images that the company only allows me to have on my computer for a maximum of 24 hours due to regulations. Every day a few hundred images come in via ...
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MaskRCNN generated masks are not sharp

I have trained my maskrcnn for object segmentation but the generated masks are not sharp. They have a blurry edges and unrefined outlines. Do you have a work around to this solve this problem?
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ValueError: Error when checking input: expected input_1 to have 4 dimensions, but got array with shape (1, 4, 4, 200, 200)

I am working on a dqn agent with a CNN which takes input of 4 images, each grey-scaled image array is of size 80x80. my model structure is like this:- ...
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In the context of deep learning , are “data reduction layers” and “pooling layers” the same concept?

From this book About this time, Dr. Le Lu joined my group. An expert in computer vision, Le brought the passion and knowledge required to apply deep learning to the challenging problems we ...
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Keras / TensorFlow 2.0: are UpSampling2D() layers the inverse of Max-Pooling?

I am trying to build a Variational Autoencoder for image data. As I employ MaxPool2D() in the encoding part, I need the reverse it in the decoder. Are ...
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Сlassification using convolutional layers

I have a simple neural network of 2 layers, recognizing 10 classes: ...
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How to correctly use depthwise convolutional layers

I am trying to speed up my CNN by replacing all convolutional layers with depthwise convolutional layers, which can require only as much as $10$% of the operations ...
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In CNN, why do we increase the number of filters in deeper Convolution layers for complex images?

I have been doing this online course Introduction to TensorFlow for AI, ML and DL. Here in one part, they were showing a CNN model for classifying human and horses. In this model, the first ...
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How many parameters in a Conv2d Layer?

I was following andrew-ng coursera course on deep learning and there's a question that has been asked there which I couldn't figure out the answer for? Suppose your input is a 300 by 300 color (RGB) ...
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Sliding window Algorithm and its convolutional implementation

I want to know why the convolution implementation of the sliding windows is equivalent to the sequential step-by-step sliding window? Why are they the same thing? I'm following Andrew NG for this: ...
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Making sense of indices in 2D convolution operations in convolutional neural networks

Referring to the answer here: https://www.quora.com/Why-are-convolutional-nets-called-so-when-they-are-actually-doing-correlations, the equation for a discrete 2D convolution is specified as: $$C(x,y)...
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Resize instead of transposed convolutions

I'm trying to build a decoder version of ResNet, i.e. one that goes from the prelogits layer and attempts to recreate the image. I can get it working by using transposed convolutions, but the quality ...
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LeCun paper on deeplearning (Nature, 2015)

As I was reading Y. LeCun's paper on Deep Learning (Nature, vol. 521, 2015), I came across a figure (the 1st one in the paper) which was associated to the backward ...
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How to approach the PlantVillage dataset?

I'm working on the PlantVillage dataset and i want to predict the type of the disease from the image of a leaf. The dataset is labeled in pairs (Type of the plant,Healthy/name of the disease). I'm ...
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Help needed implementing Convolutional Sequence-to-Sequence Network

I am trying to build convolutional Sequence-to-Sequence network that takes inputs (satellite images) and predicts the next sequence of images. As a result, we can then predict the weather. I have ...
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wavenet structure explanation

I am a beginner in deep learning and recently I am trying to understand the structure of Wavenet. (for more information, please refer to the paper http://sergeiturukin.com/2017/03/02/wavenet.html) ...
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Why use separable convolutions on one channel input?

I'm currently working on the Text Classification Guide from Google. During step 4, they create a CNN with separable convolutions for use with word embeddings: <...
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Why do we use a softmax activation function in Convolutional Autoencoders?

I have been working on an image segmentation project where I have created a convolutional autoencoder. I saw this image and implemented it using Keras. At the output layer, the author has used the ...