Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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How is parameter sharing done in CNN?

I am trying to understand the concept of parameter sharing in a convolution neural network from Parameter Sharing. I have a few confusions: Parameter sharing refers to the fact that for generating a ...
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What is the conceptual difference between convolutional neural networks and auto-encoders?

I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter: I wonder how it is different from Auto-Encoders: For me, it ...
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Dilated CNN - How to deal with odd lookbacks?

I'm currently working with a dilated CNN to solve a regression problem. I am trying to forecast 24 timesteps ahead based on 6, 12 and 24 values (lookback). However I am not sure which dilation rate ...
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Evaluating a convolutional neural network on an imbalanced (academic) dataset

I have trained a posture analysis network to classify in a video of humans recorded in public places if there is a) shake-hand between two humans, b) Standing close together that their hands touch ...
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How to change a single object detection network to a multiple object detection network?

I have trained a CNN network to detect a circle and approximate its centre and radius in an image. What I want to do now is detect the centre and radius of all the circles if there are multiple ...
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How can I get the images with the highest activation for a given unit?

I am new to machine learning. I am working on the pretrained AlexNet on Pytorch and i would like to visualize the receptive fields of a given unit U. To do that I am trying to give like 200K images as ...
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Are these visualisations the filters of the convolution layer or the convolved images with the filters?

There are several images related to convolutional networks on the Internet, an example of which I have given below My question is: are these images the weights/filters of the convolution layer (the ...
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Feeding CNN FFT of an image, a dumb idea?

My dataset consists of about 40,000 200x200px grayscale images of centered blobs bathed in noise and occasional artifacts like stripes other blobs of different shapes and sizes, fuzzy speckles and so ...
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Why and when transformers are better than CNN's in sequence modeling tasks?

Transformers have made a revolution in the domain of NLP and gave rise to a rapid boost of neural networks in a variety of language modelling problems, TTS and, recently, achieved competitive accuracy ...
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23 views

What is the best way to train neural network with imbalanced mixed data (images and structured data)?

I have structured data and image data to solve a regression problem. One sample of structured data can be related to N images. If I use only structured data, I get decent performance, but not enough ...
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CFD Reinforcement Learning Topology optimization wind tunnel

I want to create a reinforcement learning environment, designed for win tunnel simulations, where for each iteration a deep convolutional model could receive the 3D vector/scalar fields from the past ...
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How is the bias added after the convolution in a CNN?

I'm having trouble understanding how bias is added to the feature extraction convolution. I've seen people either refer to the bias as a single number that changes per filter or the whole matrix that ...
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How can I add a Sequential CNN layer on top of BERT model?

Information I'm working on a binary classification task and used BERT model from transformers library to do it using the custom model below: ...
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How to reject boxes inside each other with Non Max Suppression

I’m working on an object detection cnn, and having some issues with non max suppression. When I have a small box inside a large box, NMS is not rejecting the smaller, incorrect box, because its IOU is ...
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How to measure(classify) the speed of oncoming traffic via Computer Vision and Neural Networks?

Suppose I have different videos of the same car sometimes moving slow, sometimes moving fast, say, at 50Kmph as slow and 60Kmph as fast. (Assume the background is a green screen and the car doesn't ...
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Anything similar to BERT but for pixel-wise embedding in images

In NLP there is BERT which can take a sentence and turn it into an embedding (vector representation) which in some ways encompasses the "meaning" or more precisely the context of the ...
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Document clustering from ordered pages list

I have a series of ordered pdf pages which own to different documents. Let me give you an example: Pages: 1 2 3 4 5 6 True Pages: 1 2 | 1 2 3 4 So I have like six ordered pages, two of which from ...
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Which solutions are there to the problem of having too large activations before the softmax (or sigmoid) layer?

I'm trying to build a neural network (NN) for classification using only N-bit integers for both the activations and weights, then I will train it with some heuristic algorithm, based only on the NN ...
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Image regression - estimating sensors from images

I am trying to use images to predict the sensor data of a racing game. Being a bit of a newcomer I have multiple questions. All help/suggestion is appreciated. Dataset The dataset looks something like:...
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Neural Networks different architectures but similar training curves

I have a base neural network architecture for (3D) image sequences classification, made of conv layers followed by a LSTM and dense layers. I have 3 similar architectures : 3 Conv -> 1 LSTM -> ...
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Forecasting of spatio-temporal event data

I’m currently working on my dissertation which is centred around forecasting social conflict events. I’m using data from GDELT (Global Database of Events, Tone, and Language) to develop my forecasting ...
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Are spectral approaches to Graph Neural Networks still considered?

I've been reading several papers and reviews about Graph Neural Networks, and I still feel a bit confused about the difference between the two approaches, and also if the spatial approaches have ...
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26 views

Positional encoding in convolutional layers

Positional encoding (PE) is an essential part of the self-attention layers in the transformer architectures since without adding it in some way (fixed of learnable) to the input embeddings model has ...
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1answer
36 views

Number of classes vs number of parameters/layers?

How to estimate the number of parameters in CNN for object detection? I know that there are some well-known architectures that was trained on a lot of data (AlexNet, ResNet, VGG, GoogleLeNet). But ...
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How to implement a (3 + 2)-dimensional convolutional layer where the 2d space is "internal"?

I am trying to train a CNN to learn 5D (kind of) data. The data is structured as follows. It has three spatial dimensions [x, y, z], but it also has two "...
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How can my CNN produce an "unknown" label?

I have a dataset of 20k images of infected mango. I have built a web-based app using Flask, where a user can upload a picture, and my CNN model detects the disease. I have 6 classes in the model, ...
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A neural network to learn the connection between two totally different type of images

I have a dataset of two different type of images. Say, I have images of a person and his all 10 fingerprints. I want to create a relation between them to predict one from another. How I can do that ...
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Pixel values of segmap in multi-class semantic segmentation

I'm preparing a dataset for a multiclass semantic segmentation using U-Net like architecture. To be precise, I've got it ready but a question came to my mind. How does pixel values of a segmentation ...
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How to organizre features with attributes and sub-attributes in vectors for Neural Network?

I have a dataset of 500 instances, each instance represents a 60x60 grid, each grid cell has attributes and sub-attributes. The attribute for each cell is text value, like this: Grass, tree, building, ...
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26 views

Why the non-exploitation of edge labels in current graph convolutions "results in an overly homogeneous view of local graph neighborhoods"?

I am currently reading a paper called Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, CPPR), and I cannot understand the following sentence: We identify that the ...
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Is intersection of labels acceptable in computer vision?

I have a dataset, where objects are very close to each other. So, the question is: what is the best approach to label them? There are two possible options: mark objects so that they will not ...
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1answer
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In style transfer, why does the comparison between channels give a good sense of style?

I have been learning about Style Transfer recently. Style is defined as The correlation of activations between channels. I can't seem to understand why that would be true. Intuitively, style seems ...
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Convolutional Layer Multichannel Backpropagation Implementation

I have been working on coding a CNN in python from scratch using numpy as a semester project and I think I have successfully implemented it up to backpropagation in the MaxPool Layers. However, my ...
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1answer
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Cnn for Combination of both digits and letters(small and capital) [closed]

Hi I am new to machine learning can anyone suggest open dataset consists of both digits and letters(small,capital) I want images consisisting of both digits and letters to train my cnn model and make ...
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409 views

What is the difference between same convolution and full convolution in terms of feature map size?

In valid convolution, the size of the output shrinks at each layer. So after some point of time additional layers cannot meaningfully performs convolution. For this reason, same convolution is ...
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Why might the convolution be inappropriate when the task involves incorporating information from very distant locations in the input?

When I am reading about convolutional neural networks, I have encountered the following sentence from the textbook(page 341) that says about the limitation of the usage of the convolution in CNNs. ...
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3answers
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Is binary classification using CNN possible if the training data only consists of one class?

Is binary classification using CNN possible if the training data only consists of one class? I am working on landslide risk assessment using Convolutional Neural Networks and I want to train a network ...
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Can I get some advices on inferencing people from upwards using Yolov5?

I'm trying to inference people from upwards and count them using Yolov5. I know the controversy between yolov5 and yolov4, but for me, Yolov5 is more easier and reliable to use, also the setup. I have ...
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Origins of the name of convolutional neural networks

Convolutional neural networks (CNNs) contain convolutional layers. In modern deep learning libraries such as Tensorflow and PyTorch among others, convolutional layers are implemented by using the ...
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Should one rescale (normalize) image before or after data augmentation?

During image preprocessing pipeline, should one rescale each pixel value to [0, 1] by dividing 255 first, and then perform data transformation such as color distortion, gaussian blur? or vice versa? I ...
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How to train a model for 1 image class to detect anomaly?

I want to train a model with python over the images, and these images are for a metal product. my aim is to detect the defects, to notice if a product is a failure. what kind of architecture do you ...
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87 views

What does the "number of channels" correspond to in U-Net?

I'm studying the U-Net CNN architecture. I'm new to CNNs and am confused regarding the "number of channels". Referring to the U-Net diagram, the input image is convolved with a 3x3 mask ...
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Should I L-2 Normalise outputs in Siamese Neural Neural Network for distance computation for Triplet Loss or not?

I am building a Siamese Neural Network for Images (CNN) which uses the FaceNet's Triplet Loss as its loss function. I found a good Implementation here where we build a model and the outputs from the ...
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What is meant by "real-valued argument" in this context of the convolution operation?

Consider the following statement from Deep Learning book (p. 327, chapter 9: Convolutional Networks) In its most general form, convolution is an operation on two functions of a real-valued argument. ...
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Adversarial Attacks and interpolation methods

I am attacking a model. The model is a simple CNN and PGD is used. The model runs on 112x112 ImageNet dataset. So I first load images as 224x224 and use interpolation function to downsample it to ...
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36 views

Why is the F-beta score not increasing while the validation loss fluctuates?

I'm trying to implement a multi-label image classification from a CT scan data set. The goal of the work is to find out which CT scan image has eleven of the most common fractures if it is fractured. ...
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Can anybody just confirm whether or not my understanding of depthwise separable convolutions is correct?

I just need a simple Yes/No confirmation or to debunk my understanding of the difference between the normal convolutions and depthwise seperable convs. I have read quite a few articles and watched a ...
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CNN based model poor result [closed]

My goal is to train and evaluate The German Traffic Sign Recognition Benchmark (GTSRB) dataset using Pytorch. I downloaded the datasets from the official site GTSRB_Final_Training_Images.zip and ...
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239 views

What does "statistical efficiency" mean in this context?

Consider the following statement(s) from Deep Learning book (p. 333, chapter 9: Convolutional Networks) Convolution is thus dramatically more efficient than dense matrix multiplication in terms of ...
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37 views

Any RL approaches for this 2D space optimisation problem?

I have a list of rectangles, they are in certain order in 2D at the beginning. The task is to move them to get the boundary (rectangular) of the minimal area. It's OK to push off the dotted border as ...

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