Questions tagged [convolution]

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Winograd Convolution

For https://www.intel.ai/winograd-2/ , why use stride = 2 ? Why need to transform input image pixels ?
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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 ...
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1D convolution for uni-variate data

Every one. I have EEG dataset with 80 subjects, 3072 data points and 100 trials. This a univariate data, it mean there is only one channel. I am confused how to feed this data to convolution neural ...
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Backpropagation from a fully-connected layer to a pooling or convolution layer

I've a problem where I currently try to wrap my head around. Consider a CNN with a single convolution layer and a fully-connected layer. During the forward pass, the input is processed through the ...
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1answer
106 views

Tensorflow F-RCNN first stage input and output strides

Trying to optimize performances on Tensorflow's faster_rcnn_resnet50 (from the model zoo), I'm currently working on understanding the full .config file they provide, and I'm having a hard time with ...
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When is bias set to False?

I have been working with DC-GAN to generate pairs of images based on WGAN paper. For which I referred fastai notebook on WGAN where the bias in the network has been set to False. Jeremy Howard in his ...
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Map predictions to real text

I have read the paper "Learning to Read by Spelling" by Gutpa et al. They present a method for visual text recognition without using any paired supervisory data. In chapter 4 they describe how to ...
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Understanding Faster R-CNN

I'm having some trouble understanding the way the Faster R-CNN algorithm works. Specifically, the way the authors describe the concept of anchors. In their paper from here they describe anchors in ...
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In a CNN do filters that account for depth do so for just the initial input, or for a layers with any depth?

For example, if you have an input of an image of size 100x100x3 (where 3 is the RGB of the image), then put it through a conv layer that results in an output of <...
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conv2d function in pytorch

I'm trying to use the function torch.conv2d from Pytorch but can't get a result I understand... Here is a simple example where the kernel (filt) is the same size ...
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I am building a gesture recognition system using video data from ConGD Dataset and am not able to create the 5D array,as input to the network

I need a 5D input for my network using ConvLSTM and 3D-CNN. Converting the videos to an array of videos would contain a 5D array (Num of examples, Num of frames, FrameWidth, FrameHeight, NoOfChannels),...
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What is fractionally-strided convolution layer?

In paper Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs, in Section 3.4, it said Since, the aim of this work is to estimate high-resolution and high-quality density maps,...
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Anyone have fruit disease dataset? [closed]

I am doing a project on fruit disease recognition and classification. Anyone have an existing dataset of fruit diseases? Can you help me to find one?
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How to calculate receptive field size for -ception model?

I have a full convolution model like this: ...
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1answer
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Struggling to understand GCNNs (Graph Convolutional Neural Networks)

Although I've worked with CNN's for over a year, I am struggling to understand how GCNNs work paper on their simplification. I've read several papers, and I find myself out of my depth when they talk ...
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How is R-FCN faster than FRCNN?

During my first pass through the architecture for R-FCN, I believed that it was because their region proposal network generated regions of interest without performing the "small network slide" over ...
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Dimension of feature vectors for classification task in the DCGAN paper

I am trying to implement one of the section in the DCGAN paper (https://arxiv.org/pdf/1511.06434.pdf) i.e. Using the Discriminator network trained on ImageNet-1k as a feature extractor to classify ...
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Decovolution function

I have an image (for example (7x7x3) and a filter (3x3x3)). I convolved the image with the filter and it became a (3x3) output. If I want to do the inverse operation and want it to become the image ...