Questions tagged [convolutional-neural-networks]
For questions about convolutional neural networks, also known as CNN or ConvNet.
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Detecting object position given the relative position of another object
I know that the title might be redundant but I'm trying to understand if there is way to predict where a specific object will be if I provide a certain object as a reference.
See as an example the ...
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Can the Jacobian of a Neural Network be Full Column Rank?
Let $\mathcal{X}$ be the input data space and $\mathcal{Y}$ be the output data space.
$f: \mathcal{X} \to \mathcal{Y}$ is a function represented by some Neural Network.
Is it possible to to check if ...
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How to increase accuracy for CNN?
I have built one CNN model and applied it to chest-xray Covid 19 pneumonia dataset. I am getting the classification report as follows:
I am surprised to see that it is giving an excellent result on ...
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How to classify images which are slightly different from each other?
(I hope I'm in the right place to ask such a question.)
A robot has a fixed camera which takes images before extending its sticks to grab a box from two sides and pull it back. But if the box is not ...
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What data can I obtain from CNN model (H5 file)? [closed]
I created a CNN model and it is saved in h5 format. I used the Netron app, where I obtained the model architecture, however ...
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Is data augmentation beneficial even if the dataset is large/diverse enough?
I'm training a deep learning model to map binary images to grayscale values of the same shape. For the dataset, I can genearate one as large and diverse as I want it to be.
My question is: let's say ...
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How to use PCA to reduce the dimension of output features of convolutional layers of CNN
I am working on a hybrid CNN-SVM classification using python. I tried to get the final output features of the CNN model(flatten layer) to be fed to the SVM classifier. The output shape obtained from ...
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keras model accuracy not improving
I am trying to do multi class(16) classification, however no matter what parameters or number of layers I use my accuracy is not improving, its in 30s the max I got was 43.
I have tried early stopping ...
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How is a filter actually applied to all input channels in a ConvLayer2D
I was studying Convolutional Layers and some of their variations and I came across this post which says:
'For rgb vs greyscale, think about channels as feature maps for input layer and a filter gets ...
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During batch normalization is the mini-batch gone through twice, one to calculate the mean and variance and then to normalize them?
I am asking this question because while designing my own model, I had repeated gradient explosion issues, so I wanted to try batch normalization. I really want to understand the details and math ...
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The result of back propagation for a neural network
I have created a neural network that feeds an image into a convolutional neural net, then feeds the flattened output of this network into an artificial neural network.
I have a feeling that my ...
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Formatting Binance dataset to DeepLOB
I’m new to ML and really stuck on how to format the Order Book data from Binance to the same format of The FI-2010 dataset used in DeepLOB(Limit Order Book) Research paper. I kindly ask for your help. ...
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Does the order of a Numpy array matter for CNN classification?
Image classification of RGB images in a Convolutional Neural Network is usually done by feeding the CNN first layer with a Numpy array with dimensions: pixel array by the number of channels. Example: [...
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Using pre-trained models on image dataset that is totally different for object detection?
I have been trying out various tutorials on object detection machine learning. All the tutorials so far have been to use a pre-trained model for practical reasons when detecting objects that the pre-...
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Taxonomy of terms in DL [closed]
I am trying to teach myself DL and among other difficulties I found it quite challenging to structurize a very basic terminology vocabulary especially when it includes not only theoretical stuff but ...
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How to use strong labels in image classification?
I have a dataset where I have the labels cancer & non-cancer, and I also have localized pixel-level annotation masks of important regions/features in the images.
In a binary classification task, ...
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How does a CNN work in detecting absence of features?
I'm trying to understand how a CNN operates internally. Let's say I'm doing binary classification with 1 output neuron and a sigmoid to classify dog vs no dog. No dog meaning the image does not ...
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How to detect peak locations via Neural Networks?
As part of my masters thesis, I'm developing generative models for ECGs.
Right now, I have a Denoising Diffusion Implicit model (DDIM), that transforms random noise into a valid ECG (2s long, or 1024 ...
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ML model giving rank errors on 3D layers on converting 2D images to 3D models
i am currently working on a model to convert 2d images to 3d models through a ml model. For this i have taken into reference a research paper which had this diagrammatical flow of layers & i have ...
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CNN: Input Normalization for Time Series Data (Grad-CAM)
I trained a CNN model on univariate time series data. As I often read that it is advisable to normalize the data, e.g., using z-normalization, I started with training on z-normalized versions of the ...
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Learning an identity function with convolutional networks
I am trying to train networks to achieve what I expected to be a trivial task: learn the identity mapping. However, this is very hard to achieve, and the optimization is hard.
Moreover, I don't want ...
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Getting higher resolution images
For some image processing CNN you need to downscale your image to the input of the network. For example, some ask for images of 256x256, while others 512x512. In this process you can conserve the ...
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"Tweaking" the cost function to penalize rarer cases more severely
I have a very unbalanced data set that I am running a CNN on for regression. Most of the values are 0, while it is possible for the values to range from 0 to 32.
Is it possible to "tweak" ...
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Dealing with incomplete file sets for a CNN for medical imaging regression problem
I'm trying to solve a medical imaging regression problem using a CNN. Each of the samples in my data set consists of one, two, or three of the following file types:
flair.nii.gz
mprage.nii.gz
swi....
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Raw Audio Data Learning with CNN: Use zero-centered Input with ReLu?
I am playing around with Conv-Nets on raw audio data. Found some papers that outline different architectures but did not find a lot about the data preprocessing.
Can I use a zero-centred input for a ...
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Using rotated bounding boxes - better or not for detection?
I read a few posts here that discussed using rotated bounding boxes, but mostly about how to do it?
I was wondering if anyone has insights on;
In the most used object detection datasets, is it better ...
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what is `Normalize` for in PyTorch transfer learning tutorial?
in this pytorch tutorial, there is transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), what is the purpose of this?
(i removed it and the code still ...
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Keras Multiclass Classification - More units than classes on last layer
I am building a CNN to classify spectograms and using the following architecture currently:
...
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Help with model architecture for a racing game
I’m working on a model for a racing game using pytorch. The model gets frame from the game as input and produces a controller state as output. The dataset consists of frames from the game and ...
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How should classification on small images be done?
I want to create an image classifier that classifies very small images (16-32 pixels/side) into around 200 categories. Every category has exactly one image that defines it. The classifier should ...
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Different Kernel Initializers in my prediction layer with Transfer Learning could affect performance?
So I have this model right here and the task is to classify 3 labels.:
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Is data augmentation inducing bias?
I am using Keras to build a CNN model to classify spectograms and using the following layers:
...
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Learned kernels in CNN seem just random patterns
I am training a classification neural network using Tensorflow2 (specifications below). The training goes well (good accuracy and no overfitting, apparently). During the training I monitor the learned ...
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How do neural networks learn specific features throughout the layers?
I was reading about convolutional neural networks and I came across such an explanation:
Consider detecting features in human face. The earlier layers of neural networks learn coarse features such as ...
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Is it common that an applied deep learning research paper does not disclose any raw data and source code? [duplicate]
I think it is important for a research paper to include raw data and code for scientific replicability, verifiability, and falsifiability. However, recently, most of the research paper I read does not ...
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Why don't we use diffusion for non-graph CNNs?
I'm pretty new to graph neural networks, so please forgive me if this is a silly question.
Diffusion is a method used to improve graph CNNs, however it seems to me that general CNNs can also benefit ...
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How to calculate number of connected neurons with filter
let's say I have a conv layer i with 64 feature maps and a filter size of 3x3.
The previous conv layer i-1 has 32 feature map.
Step-size is 2 and padding 1.
My question is now how to know how many ...
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How to calculate the total number of inputs in CNN?
I search this kind of question for a while and I find many discussions involve on counting the number of parameters of a Convolutional Neural Network, but not on the inputs. Using the Fashion MNIST ...
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Is there any proper literature on the types of features that different layers of a deep neural network learn?
Let's consider a deep convolutional network. It seems that there is some consensus on the following notions:
1. Shallow layers tend to recognise more low-level features such as edges and curves.
2. ...
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How to identify important features in data?
I have a couple opportunities to write a paper, or papers over some of the neural networks I have made.
I was wondering if there are anyways to figure out why the neural network classifies the data I ...
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Deep learning feature extraction using image processing batch script?
I am planning a CNN deep learning project using photos of handwritten notes, and try to label them. I am still at the early stages, but I expect that accuracy of this neural net would improve, and ...
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Using GraphSAGE model for multigraph datasets
I checked out applications of GraphSAGE and it seems like its primarily used for single graph datasets. For example - Cora dataset - Its one big graph with 2708 nodes and 5429 edges. The model can ...
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Is there a state-of-the-art deep learning paper that uses center point regression instead of bounding box regression, for object tracking?
Almost all deep learning based object tracking methods perform bounding box regression. Siamese-based networks which are very popular for object tracking also perform bounding box regression most of ...
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How does YOLO detect the object when the object is in multiple grid cells?
I have been reading various articles and watching videos on YouTube, but I can't seem to understand one thing.
How does YOLO make a bounding box for an object if it is in multiple grid cells? For ...
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What is the advantage of adding CNN to LSTM for forecasting sequential data?
I am working with simulated sequential data and the goal is to forecast that data. Long-short-term-memory (LSTM) is one of the most advanced models to forecast time series according to this post. I ...
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DDPG agent with convolutional layers for feature extraction [closed]
I'm trying to come up with a definition of the critic for a DDPG agent in PyTorch using a CNN as a feature extractor. It is pretty straight forward for the actor model. However, for the critic model I ...
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CNNs: What does "number of filters" mean?
I understand how depth, kernel size, stride, and padding works when dealing with filters in a spatial convolution layer.
What I don't understand is "the number of filters". Does that mean ...
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How to convert color information to 1D feature vector?
We are making a classification model that takes a clip of a movie as an input and predicts who the director is. Roughly speaking, it will be a model that understands film directors' unique style.
We ...
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Why even-sized kernels are used in upscaling layers?
I have noticed that UNet and many GANs uses even-sized kernels in the upscale part of the model. I have read that at least in the GAN situation one of the reasons why we use even-sized kernels is that ...
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What are the benefits of using multiple convolutions, as opposed to one, before the pooling layer in a U-Net?
I have seen U-Nets that use a single convolution before the pooling operator and some that use two or more.
My question is, what is better? Or what are the benefits of using more or less convolutions?
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