Questions tagged [convolutional-neural-network]

A convolutional neural network is a form of neural network with an additional convolutional layer, typically used in image & audio analysis. The convolutional layer is essentially a filtering stage defined by the kernel which is used. For example, a convolutional layer could have a kernel which extracts edges from an image towards the goal of learning which objects are in a scene.

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Backtracking filter coefficients of Convolutional Neural Networks

I'm starting to learn how convolutional neural networks work, and I have a question regarding the filters. Apparently, these are randomly generated when the model is generated, and then as the data is ...
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CNN Eliminate Wrong Results

I extracted images of human faces from the videos, but the model also recorded images without faces. I wrote CNN for emotion classification. In the obvious pictures, the probability is closer to a ...
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Tensorflow.js - CNN/or autoencoder denoiser architecture?

I am new to machine learning. I have 10,000 examples of 128x256 array of values 0.0-1.0. Each example consists of a pair of a clean example and the other with noise added. I am aiming to train a CNN / ...
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What model and attributes would be good for this data?

I have the following set of data like in the picture, with 366 Temperature values for one year. The first set of data would be for training and the second one for test. I would like to detect the ...
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Binary classification from local and global feature selection

I want to train a deep leaning model, consisting of images. My question is which scenariowas chosen to train the model? scenario 1 : I train images local context on Output 1, and I train images clobal ...
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Coefficients values in filter in Convolutional Neural Networks

I'm starting to learn how convolutional neural networks work, and I have a question regarding the filters. Are these chosen manually or are they generated by the network in training? If it's the ...
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Auto encoder network

Is there any rule that we should use only deconvolution operations in decoder block of auto encoder network or we can use convolution in such way that it up-samples or mirrors the corresponding ...
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Please help me i am getting at the end of my program test

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Is Mask-RCNN a fully convolutional architecture?

Is Mask-RCNN a fully convolutional architecture (from what I see yes, because there are no dense layers but just wanted to make sure)? Thus can I feed differently sized images for inference without ...
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How to train deformable convolutions?

There is a concept in ML called deformable convolutions, instead of kernelling over rectangle filter, we use kernelling over learned shape. Whilst classic convolution is ...
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Convolutional Neural network learning curve results

Working on a convolutional neural network with 6 classes and about 1500 image per class. The model that works best for me has given the results below, in previous models I have worked on has given ...
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ResNet: Derive the gradient matrices w.r.t. W1 and W2 and backprop equation in a Residual Network

How would I go about step by step deriving stochastic gradient matrices w.r.t. W1 and W2 and backpropagation equation in a residual block that is a part of a larger ResNet network with forward ...
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Methods for combining instance observations for classification

I am working on a project where I classify tiny moving particles into a few classes (fibers, hairs, glass shards, bubbles). The particles are only a few pixels large and are observed in a few frames ...
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1D Sequence Classification using Circular Dilated Convolutional Neural Networks

I am working on a multiclass classification task on long 1D sequences. The sequence length may vary between $512$ and $512 \cdot 60$ timesteps, a slice of $100$ timesteps might look like this: What ...
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Architecture for certain points identification and detection in images

I have a dataset of thousands of images of a football field, taken with a camera from different angles and distances, from the same side of the field. I have marked the positions of all the lines ...
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Can I use low dimensional node features in graph convolutional networks?

I am trying to understand how GCNs work. For example, the well known GraphSAGE algorithm considers a graph $G$ with node features $x_i$ of dimension $n$. Then it propagates the node features over the ...
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MTCNN for face detection different result

I have a question concerning MTCNN for face detection. There are two ways to use MTCNN that I have discovered: Load a local model from their repository (...
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reducing number of kernels in CNN by using mapping just some of the input channels to each output channel?

so, I am currently learning about CNNs. And I am using pytorch to implement small models. What I don't understand, yet, is, why typically a new channel is formed by the sum of the kernel outputs of ...
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What are practical uses of MP Neurons?

Are there any practical uses of MP neurons in any industry/application or any situation where MP neuron outperforms in some metric other methods? Or is it only just used in teaching as a basis to ...
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Can I use Variational Autoencoder/GAN for image manipulation?

I have a CT image with the tumor and the corresponding Radiotherapy image. I want to predict the CT-Image with the corresponding change. For my training, I do have input CT image, Radiation therapy ...
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Why does neural network need loss as scalar?

I have a loss function that's a weighted cross entropy loss for binary classification ...
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Suggestions for labeling regression data to improve model accuracy

I'm working on a convolutional neural network that should predict up to 3 (x,y) coordinate pairs representing the waypoints of a concrete path, given an input image. This network will be used to help ...
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Is it possible (if yes, then how) to provide the semantic segmentation results on the Original Size of the input image?

Just like this website, it does not matter what shape of image you provide them, instead of giving you a fixed size output, they'll segment your image and provide you the resulting image with original ...
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What is the best strategy for labeling and training this CNN?

I want to start off by saying that I am completely new to machine learning/neural network. This is part of a research class in my high school and everything I know has been self thought. Now that my ...
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Keras autoencoder gives same output

I have a convolutional auto-encoder which is created like this: ...
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What is meant by non-linearity in Convolutional Neural Networks? And why do we focus on removing it entirely?

I am aware of the working of ReLU that it turns every negative value to zero and does not effect any positive value, but what confuses me is this: what is actually meant by non-linearity in feature ...
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Misconception about Cross entropy

I have difficulty understanding the cross entropy function, my confusion arises when it get to implementation in the code. sor cross entrop is defined as $-\sum_{x} p(x)log(q(x))$ where $p(x)$ is the ...
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Baseline result is much better than state-of-the-art model

I am researching about Deep Learning based Intrusion Detection System. I found a paper on a well-known journal, which is considered as a state-of-the-art method in this research area, because it got ...
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Neural network architecture to automatically crop a photo of a paper sheet

With an RGB image of a paper sheet with text, I want to obtain an output image which is cropped and deskewed. Example of input: I have tried non-AI tools (such as ...
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Stacking models using keras.layers.Concatenate with different input shapes

I have concatenated two models that uses different inputs. The first model uses input of shape (1, 33). The second model uses a feature set of dimension (1, 1024). I have a mapping function that ...
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Effect of image scaling on convnet

Question 1 I have an image classification dataset which consists of images with varying aspect ratios. My classifier is a ResNet. As of now, I just squish the images to 225 X 225 and train the ...
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Define output size of a CNN

I am trying to build a CNN model that will reverse images from JPEG to RAW, and for that I need to take a (2*N, 2*M, 3) array into a (N, M, 4) for N, M some integers (i.e. 128x128). 3 stands for 3 ...
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Discover the geographical position of weather historical data using weather historical predictions

I have too little experience in neural networks and I wanted to solve a problem that I don't know how to raise. On the one side, I have a 1 year hourly historical data of the temperature in a specific ...
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Variational Autoencoder - ValueError: No gradients provided for any variable (TensorFlow2.6)

I am implementing a toy Variational Autoencoder in TensorFlow 2.6, Python 3.9 for MNIST dataset. The code is: ...
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Estimating joint angle using machine learning methods

I am currently working on a project which uses IMU sensor data in order to predict the Knee Angle at each time point. My input data consists of 9 sensors, I plotted an example section below. The goal ...
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adding attention doesn't improve accuracy?

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Consistency between multiple word predictions in a single NLP sentence

Considering a model which predicts multiple missing words in a sentence: The ___ is preparing the ___ in the ___ There is no pre-existing context in this masked ...
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Model Overfitting in text classification how to solve?

This is my CNN model i am doing text classification on mental health social media data. the model is overfitting as validation loss is much greater than training loss. There are three columns(Text, ...
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Where can I find a good image repair model?

Where can I find a powerful learning-based algorithm for image inpainting so that I can, say, remove certain pixels from an image, have the model fill in those pixels, and return the new image all in ...
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1D Convolution on multiple channels of varying length

Every datapoint in my dataset consists of 3 time series. The data in the time series is discretized into equal time-bins but the 3 time series were measured for varying length. Time series 1 has 10 ...
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Split a tensorflow (.pb) model into two separate models

I have a tensorflow (.pb) model, it has has some convolution and maxpooling layers followed by dense layers. I would like to split it in two pats. The first containing the convolutions/maxpooling part,...
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What happens if you don't include any activation function on hidden classification layers?

What happens if we don't apply an activation function to the classification hidden layers and apply it only for the final output layer (Sigmoid, Softmax)? I'm asking this because I have trained a CNN ...
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Keras CNN -Identify Non-Related Images

I have a CNN classification problem, where the 2 classes are mutually exclusive. I am using Keras keras.losses.BinaryCrossentropy(from_logits=False) and I am ...
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Inbetween CNN and MLP: neural network architecture for "close to convolutional" problem?

I am looking to approximate an (expensive to calculate precisely) forward problem using a NN. Input and output are vectors of identical length. Although not linear, the output somewhat resembles a ...
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Keras CNN low training and val acc

I don't what is wrong with my current network architecture maybe some of you can help. I have a dataset that is highly imbalanced so i have implemented a datagenerator which balances the image data so ...
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CNN auto-encoder performs much worse than Fully connected auto-encoder

I am trying to develop a one-class classifier, that will learn regular examples, and, hopefully, will have hard times reconstructing anomaly observations. I have 1D signals which I tried to ...
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Conv2d for image with additional features as input layer

I would like to train a model with Keras and TensorFlow. My input consists of images and some additional features. I would like to use conv2d for the images and dense for the other inputs. The ...
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Binary classifier only predicting one class after undersampling my data

I'm trying to classify doublets of images into 2 categories. My initial results where poor and I thought that this was because my classes were heavily imbalance (about 88-12). I've under sampled my ...
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Custom Tensorflow loss function that disincentivizes all black pixels

I'm training a Tensorflow model that receives an image and segments the image into foreground and background. That is, if the input image is w x h x 3, then the ...
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Neural network training for two vs. many classes

Suppose I want to train a convolutional neural network to distinguish not between dogs and cats, but between images of dogs and images of any feline – cat, lion, tiger, leopard, cheetah, etc. ...
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