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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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What would be the convolutional layer output by keras.layers.Conv2D when conv output is fractional?

I have input ($n=224$), strides ($s=4$), filter size ($k=11$) and no padding which gives me a fractional conv output: $$\texttt{conv output} = (n-k+2p)/s + 1 = 54....
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the Detailed Architecture of EfficientNetV2-B2

I'm currently studying different neural network architectures and I'm particularly interested in EfficientNetV2-B2. I understand that this model is an improved version of the original EfficientNet, ...
WILLY WIJAYA's user avatar
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Are these generated from my code the so called feature maps?

I assume that the way people build which activations detect specific pieces from an image is by executing the network and extracting the results at each layer; when the output is from a convolutional ...
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CNN matrices shape for time series data processing

I would like to ask you for advice regarding CNNs for analysing 60000x16 data (single input) - time series records from 16 channels. I did some research on this and my initial idea was to use CNN with ...
kalmary's user avatar
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Can we use convnets to learn the masked letter in a word?

I'm interested in training a CNN to learn the relationship between a word with a single masked letter and that masked letter. For example, if my model is $M$ and the input is "he-lo", it ...
Spencer Gibson's user avatar
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How to use Conv2D for make predictions on spatio-temporal data (non-image)?

I have multivariate time series data consists of 4 independent variables, 1 dependent variable (target variable), and spatial data (latitude and longitude). The data is taken from 5 different cities, ...
Riri Ana's user avatar
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Getting 99-100% accuracy on my training/validation data but performs bad on completely new data

I have a large dataset of the ASL (American Sign Language). I split this data into 70:15:15 for train, validation, test. I then trained a CNN model on it, where I trained using the 70%, and evaluated ...
codinator's user avatar
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How can you determine whether a feature vector extracted from an image is representative for a specific task?

In the task of scoring video frames based on their features for video summarization project, if the frame features are extracted using pretrained CNNs such as GoogleNet, VGG, and ResNet, how can I ...
moha tech's user avatar
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Classification of intervals in time series data of multiple instances

I have a problem that I am trying to frame. I have signal data from ECG (a classic signal over time data). A close example here: https://github.com/jjongjjong/ECG_segmentation_1DUnet I am basically ...
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Image classification metrics

I have been working on an image classification task using CNNs and getting some puzzling results. My training, validation and test loss keep going down with epochs and are comparable. So this might ...
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Impact of Pixel Normalization Technique on Weights, Gradients, and Activations in Neural Network

There are different ways to process an image either before or during the training of a neural network trained to take in image inputs. Some of the pixel adjustment techniques used: Scaling each pixel ...
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Sensible neural network architecture for image stitching?

I am currently trying to design a neural network for image stitching, but I am having trouble coming up with a neural network architecture that seems suitable for the job. The input to my neural ...
Tue's user avatar
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Should the testing data be uniquely distinct and come from different source/dataset than the training data?

I am building an audio classification system using CNN. My dataset consists of different audio I have recorded and spliced to equal time lengths. Like with any other common ML or DL tasks, I am to ...
Flash's user avatar
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Extremely Small Output Weight Values in Echo State Network

I have an echo state network that is producing an output weight matrix with extremely small output weights (on the order of 10^-200). Ideally, these weights should be within a more reasonable interval,...
Jonathan Frutschy's user avatar
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Teach a multiclass CNN classifier on images with highlighted target

I'm building a classifier for objects located in images. There are really good networks for semantic segmentation, like "segment-anything", and I wonder is it possible to teach a CNN, i.e., ...
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The relationship between ridge regularization and CNN Data Augmentation

In Chapter 10.3.4 of Introduction to Statistical Learning with Applications in Python by James et al. there is a sentence on data augmentation for CNNs (adding natural transformations of images into ...
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Global Average Pooling layer and location information

I've got a question about the usage of Global Average Pooling layer (GAP). I am working on the classification of sea level pressure data, where obviously the location of high/low pressure system ...
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Increasing the clarity in the tasks of image generation using CNN

What methods exist to improve the quality of generated images and the clarity of contours in the tasks of image denoising/debluring (using CNN), style transfer etc? I am interested in approaches that ...
Alimagadov K.'s user avatar
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Regression model with 3 outputs and different dataset to train each of them

In general I want to predict 3 parameters ISO, aperture and exposure from photo. I was thinking about using cnn with regression. Despite of fact that this is hard task, my minor problem is with ...
swaxkidrauh's user avatar
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How to feed multi-channel spectrograms to Deep Neural Network?

I am using a 14 channel EEG device. To do away with the need for any handcrafted features, I wish to implement an ML classification task with the EEG data collected using deep neural networks (such as ...
Anantha Krishnan's user avatar
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Seeking Guidance on Constrained Input Modeling for Soil Moisture Correction Using Rainfall Observations

I find myself immersed in the intricacies of working with 2D modeled fields (images) representing soil moisture in regions where direct observations are unfortunately absent. However, there is a ...
Seyed Omid Nabavi's user avatar
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Question on a paper which talks about stacking several fully connected layers of 2 neural networks

So to preface, my knowledge on neural networks is very limited, and I've had a very difficult time trying to comprehend the details of this paper. My background is in maths, and I've created a ...
Rowan Harley's user avatar
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Pre-trained CNN models for high resolution images

I am trying to fine-tune a model with my dataset using Keras. However instead of using the default input shape (224 x 224 x 3) for almost all of the available models, I want to set the input shape as ...
Berk Çam's user avatar
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How to detect small details in high resolution images using CNNs?

I have the goal of training a CNN model that can detect if clothing items has any defects (holes, stains etc.) on them. I am using Keras to accomplish this. I will use the model for image ...
Berk Çam's user avatar
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Why is the maximum path length for convolutional layer $O(n/k)$ in attention is all you need paper?

In the table-1 third row it is being mentioned. Why is it $O(n/k)$? Take for example 1d convolution of 2 over 9 tokens with stride $1$. It won't be $n/k$ or $9/2=4.5$ rather it would be roughly $n-1$ ...
user404316's user avatar
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1 answer
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Understanding the function of attention layers in a convolutional neural network (U-Net in a diffusion model)

I am trying to understand the neural network architecture used by Ho et al. in "Denoising Diffusion Probabilistic Models" (paper, source code). They include self-attention layers in the ...
Rational Function's user avatar
2 votes
3 answers
132 views

Neural net performs inline with linear regression, how can I improve it? [duplicate]

I have a regression problem with around 1 million samples and 400 features (some not too meaningful and/or are redundant) and 1 target variable. I have been trying very hard to design a neural network ...
Jim's user avatar
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Why the training accuracy stays high but validation accuracy does not change?

I have a binary classification problem. I get ROI mammogram images and then apply a decomposition algorithm and as output I get 5 images which summation of them results in the original image. Now, ...
Nmgh's user avatar
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How to interprete a 1 dimensional CNN

There is quite a bit of info to find about interpreting CNNs using 2 dimensional convolution layers. But not much about CNNs using one dimensional layers. How can one go about finding out which ...
Viktor VN's user avatar
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CNN classifier for cytometric data always has a similar accuracy, regardless of complexity, number of epochs or size of the layers [duplicate]

Background: I am making a convolutional neural network (CNN) to try and classify cytometric data. This data has a shape (num_cells, num_markers). Additional ...
Viktor VN's user avatar
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Can Convolutional Neural Networks (CNNs) be accurately represented as directed acyclic graphs (DAGs)?

While it's evident that fully connected layers, even with skip connections, form DAGs, the shared weights in convolutional layers introduce complexity. How does the unique structure of convolutional ...
roymustang's user avatar
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The accuracy of my validation set is always the same

I am training a CNN model which is used for a multi-label classification task. My training data set has 5000 data points, every data point is a 100000 long 1-D array. So the shape of my training set ...
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Searching for a proper way to reduce the dimensionality of activations from a CNN

I am conducting an analysis to compare the similarities between different images across early and late layers in a CNN. The model I am working with is the pretrained DenseNet121 that comes with ...
LI Bing's user avatar
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Convolutional Neural Networks - Flattening with multiple feature maps

I have a very simple question about CNNs, which I unfortunately couldn't find an explanation for. Imagine we have a CNN, that has four filters (eg right, left, top, bottom edges) each of those outputs ...
Michal Gally's user avatar
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Fully Convolutional Networks: Fully Connected Layers as Convolutional Layers

I'm reading the paper "The Fully Convolutional Network" and I don't understand this passage in 3.1: Typical recognition nets, including LeNet [21], AlexNet [19], and its deeper successors [...
user0906's user avatar
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How can I feed data files of different sizes to a CNN?

I have 5022 data files. I want to feed them into a CNN model. However, the lengths of data files vary from, say, 50 to 1015 rows (the number of columns is constant). In the case of image files, we can ...
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Help with multimodal (hydra) CNN architecture

I am trying to learn hydra like CNN architecture based on publication using AFLW dataset. I replicated results from repository and its fine. Moving forward, I added one another head/regression task ...
JohnDoe's user avatar
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How can I reduce fluctuations in my validation accuracy?

I'm training a CNN with pictures data for binary classification and while my training accuracy increases, my validation accuracy keeps fluctuating between small and high values of accuracy. I have a ...
KClav's user avatar
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Backpropagation Derivation for CNNs

I’m looking for a good reference (be that textbook, website, article…) that goes through a derivation of backpropagation in a multi-channel convolutional neural network. I’m also looking for a ...
oweydd's user avatar
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Formula/Proof: How many times must maxpooling (3x3 kernel, stride 2x2) be applied for a random binary mask (dxd size) to have only 1 values?

Given a d x d array, 1% of which contains the value 1 and all remaining locations contain the value 0. (e.g. a 128 x 128 array would have 164 values equal to 1 and 16220 values equal to 0). What would ...
Yatagarasu50469's user avatar
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1 answer
55 views

Creating a CNN model for multi-output prediction where one target variable is categorical, and others are numeric

I want to create a simple CNN model for multi-output prediction. The predicted values are four numeric values (all between 0-1) and one categorical value (4 classes). When I try to create a model ...
Dkasi's user avatar
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How to Change Architecture of DCGans?

https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html I was refering this notebook but default size is 64*64 I want to change architecture to 256 or 512 Can anyone help me with training ...
user20332627's user avatar
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VAE for Motion Sequence Generation - Convergence Issue with Scheduled Sampling

I implemented a Variational Autoencoder (VAE) in PyTorch for motion sequence generation using human pose data (joint angles and angular velocities in radians) from the CMU dataset. The VAE ...
RTn's user avatar
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2 votes
1 answer
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Is batching needed for the test set?

I'm just starting to learn about CNN (convolutional neural networks). Does the test data also need to be divided into batches, similar to how it's done with the training data?
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Image processing: Inferre rotation angle of tilted rectangle on noisy background

I have many "grayscale" images i.e. 2d-arrays like the following: i.e. dark rectangles which are tilted by an angle $\alpha \in [-3^\circ, 3^\circ]$ and a bright but noisy background. I ...
cknoll's user avatar
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3 votes
1 answer
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Probability in a classification problem [duplicate]

In a classification problem (let's say of two categories, cat and dog) with a softmax output, does the probability have any physical meaning other than assigning the category to the input based on the ...
Shaz's user avatar
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2 answers
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Can not understand a column in a paper about CNNs

I am reading the SqueezeNet paper and I do not get the parameter depth here: There isn't a description under the table, and the only extra mention of the parameter is that it means the number of ...
Minsky's user avatar
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CNN kernels similarity

I know some theory about deep neural network, cnn and back propagation in general. I am fascinated by the power of these technologies. I try to understand also the math aspects. For example the fact ...
Luca's user avatar
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My CNN seems to reach a certain loss threshold, then stops learning from there [duplicate]

I wasn't sure how to phrase this question, but yeah. My CNN (code here) keeps getting up to an accuracy of around 66% until it stops learning anymore. It just keeps fluctuating around the 66% range. I'...
ExplorPython's user avatar
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Shape of the flattened vector in CNN

If I have a max-pooled convolution layer of dimension (5,5,4), means 4 no. of 5x5 feature maps, what will be the shape of the flattened vector after applying ...
mainak mukherjee's user avatar

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