Questions tagged [convolution]

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

Filter by
Sorted by
Tagged with
2
votes
0answers
12 views

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 ...
1
vote
1answer
27 views

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 ...
0
votes
0answers
20 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,...
0
votes
0answers
7 views

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 ...
0
votes
0answers
6 views

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 ...
0
votes
2answers
37 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, ...
1
vote
0answers
12 views

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 ...
0
votes
1answer
21 views

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: ...
2
votes
2answers
45 views

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 ...
1
vote
0answers
10 views

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 ...
1
vote
2answers
25 views

Convolution layer dimensions in deeper layers?

I am trying to understand the CNN network dimensions: ...
0
votes
0answers
13 views

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 ...
0
votes
1answer
21 views

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.
1
vote
0answers
22 views

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 ...
1
vote
1answer
32 views

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: ...
1
vote
0answers
13 views

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 ...
5
votes
1answer
226 views

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 ...
3
votes
1answer
63 views

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 ...
0
votes
0answers
14 views

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 ...
0
votes
0answers
29 views

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 ...
2
votes
2answers
62 views

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 ...
3
votes
0answers
16 views

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 ...
3
votes
1answer
56 views

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 ...
1
vote
1answer
26 views

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: ...
1
vote
0answers
32 views

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 ...
0
votes
0answers
18 views

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 ...
0
votes
1answer
50 views

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 ...
0
votes
0answers
15 views

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 ...
2
votes
1answer
58 views

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 ...
2
votes
1answer
43 views

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 ...
0
votes
0answers
6 views

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?
0
votes
0answers
17 views

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 ...
1
vote
2answers
26 views

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 ...
2
votes
0answers
16 views

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 ...
0
votes
0answers
12 views

Precision-Recall for CNN place recognition problem

Given 3450 query and 3450 reference images in a place recognition problem, I plot the ...
1
vote
0answers
21 views

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 ...
2
votes
1answer
430 views

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: ...
0
votes
1answer
56 views

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 ...
0
votes
2answers
19 views

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 ...
0
votes
1answer
15 views

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 ...
0
votes
0answers
15 views

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 ...
0
votes
1answer
13 views

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
2
votes
2answers
83 views

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 (...
1
vote
0answers
58 views

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 ...
0
votes
0answers
11 views

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 ...
0
votes
0answers
14 views

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 ...
0
votes
0answers
49 views

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 ...
0
votes
0answers
68 views

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 ...
1
vote
1answer
215 views

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 ...
0
votes
1answer
19 views

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 ...

1 2 3 4 5