Questions tagged [conv-neural-network]

Convolutional Neural Networks are a type of neural network in which only subsets of possible connections between layers exist to create overlapping regions. They are commonly used for visual tasks.

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Brain tumour detection using CNN

I have a fairly basic mathematical and implementational understanding of ML algorithms and CNNs, and I am trying to think of an approach for this task: https://www.kaggle.com/c/rsna-miccai-brain-tumor-...
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Why is my 1D CNN so bad?

TLDR: My 1D CNN is doing a really bad job classifying graphs. Here's more context: Note: I've tried adopting the advice listed here and here, but my CNN hasn't stopped overfitting. I've already tried ...
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Difference between graph neural network and graph convolutional network

Which characteristics my neural network (NN) model should have to be considered as a graph convolutional network (GCN) instead of a graph neural network (GNN)? I know that GCN is a variant of GNN, but ...
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What type of neuron values should we use for similarity functions when comparing two neural network representations?

I asked a related question on how to center the input to similarity functions designed to compare neural networks and was wondering if we should input the neurons after the activations or the neurons ...
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How should one normalize activations of batches before passing them through a similarity/distance function? (like CCA, CKA, OP)

I was reading these two paper: https://arxiv.org/abs/1905.00414 https://arxiv.org/abs/2108.01661 and they mention that before computing the similarity of two layers in a neural network ...
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Same loss and accuracy on epochs

I originally had a Binary CNN which was working at 98% after fixing what I think was overfitting. I then changed it to a classification CNN with 4 classes and I have had troubles ever since. I split ...
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Why does the number of filters does not determine the output size in the CNN layer?

The formula to determine the output size is [(W−K+2P)/S]+1. Here the number of filters used is not significant for determining the ouput size, I was wondering why? Is it because Each filter is ...
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Adding features to Sequences for Dense or LSTM

I am confused about the best way to add features to a CNN or LSTM model. Say I have input features where each example is an array of len 10: say [3, 5, 8, 9, 1, 7, 44, 12, 11, 6] and this goes in an ...
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How does Residual Shift work in the Temporal Shift Paper?

While reading this paper, I came across the paragraph, "Instead of inserting it in-place, we put the TSM inside the residual branch in a residual block. We denote such version of shift as ...
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Transfer learning on images with higher dynamic range

Is it possible to fine-tune a CNN-based model previously trained on grey-scale images with 8 bits depth [0 ~ 2^8] to fit a 16 bits depth [0 ~ 2^16] images? if there is any research paper that confirm ...
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Can we add dilated layers to Graph Neural Nets?

GNNs are not as deep as CNNs (due to over-smoothing and other factors). So could we replace a graph convolutional layer $$ h_i^{(l)} = \sigma\left(w^{(l)} \cdot \text{Agg}\left\{ h_j^{(l-1)},\forall j ...
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Which model is more efficient and why?

Suppose, I have two NN models: CNN model Sequential NN model They are solving the same problem. The data points have the same number of features. In the case of #1, we used 0.6 million data points, ...
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Good architecture/approach for encoding text

I want to create a model that efficiently encodes text for retrieving images that match the description given in the text. I have extracted features of images through VGG19 model(4096 features for ...
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Advice for machine learning task with chemical data using CNN

I am relatively new to CNNs, and I'm working on a machine learning classification problem with chemical data. I'm looking for advice on 1) how to structure the input data, and 2) architecture for the ...
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Face recognition : validation loss is not decreasing and accuracy is not increasing [duplicate]

So basically I've been trying to use CNN for face recognition. And I think that my model is suffering from overfitting since the validation loss is not decreasing yet the training is doing well. For ...
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Could I apply Spatial or simple Dropout before or after Adaptive Average Pooling or Global Average Pooling?

I'm working on a 1D CNN and I want to apply a Monte Carlo Dropout in order to get the mean of the predictions for each instance (as well as the variance, and entropy later on). The network topology ...
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What level prediction (node- or graph-level) is appropriate for my graph network problem?

I have experience in neural networks and just started exploring if graph NN is appropriate for my problem. So, I have an undirected graph with nodes separated within a specified distance, say d, being ...
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How to better regularize a 1D-CNN for Gesture Recognition Time Series Data?

I’m currently developing a 1D-CNN to work on a Gesture Recognition approach and I’m trying to solve the problem of how to regularize correctly the Neural Network and recognize the known unknowns while ...
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Is the depth in CNN network same as chanels for images?

I was studying from a Stanford lecture for CNN and they used W * H * D for images. When using PyTorch, we use channels for images. Is this depth the same as the channels for the initial convolutional ...
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What are the meanings of Node Classification, Link Prediction, Graph Classification in Graph Neural Network?

I am currently studying Graph Neural Network but I have some difficulty in understanding what I can do after having studied Graph Neural Network. From having gained a bit of understand in Graph ...
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What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?

I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application ...
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What are the disadvantages of the translational invariance in CNN?

I have been studying CNN, and realized no one had talked about the property of translational invariance in a bad/negative way. I'm curious if this property is "always beneficial" because it ...
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Comparing CNNs with same training data but different number of classes

I train a CNN image classifier for classes tabby cat, tiger cat and dog. Since ...
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How Are Kernel Weights Trained in 1-D CNN's with Multi-dimensional Input?

I have far from a perfect understanding of how 1-D convolution neural networks learn, but I think I understand how the kernel operates on 1-D input data. How does 1-D convolution work with multi-...
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Methods to reduce CapsNet parameters

Kind of a long shot, but I was wondering if anyone had any experience with Capsule Networks. I'm fairly new to them and have encountered the issue of a massive network (over 200 million parameters) ...
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U-net with non-square input images

I am trying to implement U-net in Python using Keras with my biomedical data. Therefore I am using the following code as an example: https://github.com/bnsreenu/python_for_microscopists/blob/master/...
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Transpose-Convolution related question (neural networks)

I know that convolution can be expressed with matrix notation, by using the Toeplitz matrix with elements arranged in a special order using the elements of the kernel. Is there anything similar for ...
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Is this transformation to the input of an RNN "valid"?

I'm reading through some pytorch code published as part of a research paper. The input data is of the shape (batch_size, number_of_time_steps, number_of_predictor_variables, height, width). The code ...
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Understanding number of rnn units in RNN networks

I am trying to learn about recurrent neural networks from here. There are rnn_units = 1024 in the model and each batch contains ...
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Misconception about ReLu

I have already gone through the post and this post, but they didn't clear my doubt. Let us say if I have a deep neural network like (having more layers about 50): Now, my question is: If I'm using an ...
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How does cross-correlation relate to convolutional neural networks [duplicate]

I am currently doing research on cross-correlation with two representations of images from convolutional neural network (CNN). Please give me some further idea with the correlation concept in CNN ...
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Clarifying terminology of concepts in a CNN

Despite having dealt with Machine Learning for a few years now, I still find myself sporadically confused with elementary terminology, especially when it comes to Convolutional Neural Networks, and I'...
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Loss function increases with Epochs for 2DConv

Background Hi all. I'm new to Machine Learning & Cross Validated, so please let me know if I made any mistakes. Any advice to point me in the right direction would be greatly appreciated. Problem ...
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Edge Detection Convolution Intuition

I was learning about convolution and how filtering helps us to detect an edge in an image;however I still cannot not understand how the convolution process in the image below does this. I understand ...
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What are quadratic patches in Livne et al 2019's paper on convolutional neural networks?

As I was trying to make sense of this paper, I came to learn about the term quadratic patches in Livne et al 2019. This is a fairly new term to me. Could anyone ...
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Is the density of PixelCNN normalized?

PixelCNN++ constructs a model distribution $p(x)$ over images $x\in\mathbb{R}^{n\times n}$ as a product of conditional distributions over pixels $$p(x)=p(x_1,...,x_{n^2})=\prod_{i=1}^{n^2} p(x_i| x_1,....
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Is a convolution with $n$ input channels, $n$ filters and $n$ groups the same as depthwise convolution?

Let's say we have an input tensor $X$ with $n$ channels and a grouped convolution with $n$ groups and $n$ filters which produces an output tensor $Y$ with $n$ channels. Will this convolution ...
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Why does strict translation invariance get destroyed after doing zero padding in the images?

While reading the paper on the Siamese Visual Tracking approach, I stumbled on this concept of Strict Translation Invariance in CNNs. The paper quotes: "Concretely speaking, one reason is that ...
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The same hyperparameters when excluding some categories for a DL model?

For my Deep Learning model#1, I classify images into 3 categories (e.g: Dogs, Cats, Worms). Then, I would like to train model#2 to classify only 2 of the 3 categories, i.e. excluding one of the ...
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Why Resnet18 works better than VGG16 architecture?

Based on https://towardsdatascience.com/vggnet-vs-resnet-924e9573ca5c and my understanding, Resnet18 architecture is faster because it is based on skip connections which not allow vanishing gradient ...
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Would preprocessing an image with multiple filters speed up training?

I came across this post - https://stackoverflow.com/questions/23470229/why-do-i-must-use-sobel-operator/23478399#23478399, where they use a Sobel filter for improving handwritten digit classifiers. ...
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Definition of CNN Layers

I hope you are all well. I have to present an architecture of 1D CNN today and I am a bit confused. I have a 1D Convolutional neural network Consisting of input data, 3 fully connected 1D convolution ...
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CNN Notation "conv4_3"

When a CNN layer is referred as convX_Y (e.g conv4_3), what do X and Y stand for? Is it simply just the layer name, and nothing to do with the details of the layer? For example: in the SSD paper by ...
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How to explain huge bias on unseen data?

I've trained a CNN to do a binary classification based on 2D radar spectra. I've tried different dataset sizes (reaching 200.000 samples per class) and always make sure that the classes are ...
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What are the impacts of different learning rates on this model and why does it keep overfitting?

I am training a custom CNN model with 10 Layers based on this paper: https://journals.sagepub.com/doi/full/10.1177/1558925019897396 I have two classes (defect integrated circuit & non defect ...
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How Convolutional layer work exaclty in RGB image processing?

I'm studying convolutional layers and I'm pretty confused. Supposing that I give to my network (CNN) an RGB image, so an image with three channels. Since the image has 3 channels, then the kernels ...
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How to use BinaryCrossEntropy (intutively) for Generator Network in DCGAN model?

TL;DR: Can someone tell me intuition working on BCE loss in the generator, specially for RGB as each pixel is having 3 values i.e. a list of values rather than just ...
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How to modify an NN's loss and/or optimizer for regression where dataset is mostly 0?

Currently, I am using a U-Net with skips to predict images. These images are based on data from 30 minutes prior. Most of the true image is filled with 0, with a range of approximately [0,50]. The ...
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Siamese Network for face comparison wont learn, accuracy stuck on 0.5, and loss stuck too [duplicate]

I'm trying to train a siamese network which contains a CNN and an embedding layer at the end to yield 2 similar (close) vectors for 2 images of the same person. I'm using the LFW_Cropped dataset, and ...
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visual of how to find the feature maps of two input with 3 filters in cnn

1st layer of CNN : We have an input and we apply two filters on the input to get 2 feature maps. now in my knowledge, we'll use these two feature maps as an input in 2nd layer of CNN. if we apply 3 ...

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