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.

Filter by
Sorted by
Tagged with
0
votes
0answers
5 views

Should I use batch normalization synchronization across multiple GPUs for classification training

I'm wondering if for regular classification training it's crucial to use batch normalization synchronization when training on multiple GPUs. Many papers report improved model quality when training ...
0
votes
0answers
16 views

Optical flow models (FlowNet) training/finetuning process

I'm reading about optical flow models, particularly FlowNet and PWC-Net. I thought I understood how training and finetuning are being done, but I don't believe so anymore after trying to understand ...
1
vote
0answers
6 views

Which model to select of two similarly performing, models with similar architecture and number of parameters, but different depths

I am training U-Net models for two-class semantic segmentation (foreground/background). I have tested different depths of the U-Net along with different number of filters in the first conv-layer (the ...
1
vote
0answers
13 views

Fine-tuning VGG-Face for Facial Expression Recognition on FER2013 - Grayscale vs RGB Images

I am experimenting with Facial Expression Recognition and want to use a pretrained CNN model and a multi-stage fine tuning strategy to deal with scarce data. I came across the work of Knyazev et al. (...
0
votes
0answers
24 views

What image filter is used in max pooling in image processing

I am curious to know which filter is used in to do max pooling? I am aware that it is a Deconvolution layer. As it takes the maximum value across all pixels - would it use an nonlinear area filter? As ...
0
votes
0answers
3 views

Graph to graph mapping

I am interested in learning a transformation of large graphs (> 1mio nodes, > 20mio edges) onto other graphs. I am aware of GNNs, but the embedding of the original graph into some $\mathbb{R}^n$ ...
1
vote
0answers
9 views

What is the difference between the receptive field and a patch?

In this question and answer it is beautifully described what a patch is. What's a "patch" in CNN? However, for me, following the explanation in the link, it seems like a patch and the ...
0
votes
0answers
53 views

What is MBConv that EfficientNetv2 is using?

EfficinetNetV2 uses MBConv/Fused-MBConv as a part of it's architecture. There is no clarity of what these operations actually are from the paper (nor from the references). It appears that it is some ...
0
votes
0answers
5 views

Training Network on Coarse and Dense Images

I remember watching video/reading a paper (not sure which) where a SOTA accuracy and speed was obtained by training a CNN based network on highly pixelated (low pixel density) images and then fine ...
2
votes
0answers
20 views

What could be the cause of too much fluctuations of validation loss and accuracy?

I am doing a binary-classification problem and I have 7600 images, 3800 for each class. I am wondering why the accuracy and loss plots look like these. Is this fine? or is this a symptom of some ...
0
votes
1answer
33 views

this is not overfitting but something else, right?

I trained a VAE on a dataset containing 1k images. The VAE itself is convolutional, downsamples 256x256 rgb images four times before reconstruction and uses both relu and BatchNorm Layers as well as ...
0
votes
0answers
15 views

CNN - Is L2 counted at every layer?

I build one CNN model with this code: ...
0
votes
0answers
8 views

When my training loss goes down, my evaluation loss goes up. What is this a symptom of? [duplicate]

I'm training a multi-label image classifier with a dataset of 4096 images and a network model based off of DenseNet, using an SGD optimizer. The pink graph has a learning rate of 0.01, and the blue ...
1
vote
0answers
23 views

Net not learning in MINST with peculiar cost function

I've been 12 hours non stop trying to solve this. Defeated, I came here to see if someone can help: I'm training a LeNet architecture with MNIST in Pytorch, using the loss function from eq(5) this ...
0
votes
0answers
16 views

U-Net with more Parameters learns faster than with reduced Width

For testing reasons I implemented a U-Net with three different widths, reducing the number of feature channels and filter operations. The three variants have 31, 8 and 2 million parameters. When I ...
0
votes
0answers
24 views

Why are Deep CNN's mesh dependent?(Fourier Neural Operators article)

I am reading the article named "Fourier Neural Operator for Parametric Partial Differential Equations" by Zongyi Li et al. In the very first page, there is a paragragh about Finite ...
0
votes
0answers
17 views

What will be the Precision and Recall value for Faster RCNN?

I am using TensorFlow object detection API for Faster RCNN object detector. Now I want to measure the performance of my model, so I have evaluated it using the code below for getting the mAP, ...
0
votes
0answers
16 views

Making use of spatial context for a regression problem (possibly CNNs)?

I have as predictor variables geospatial raster data (~30 layers of various height models, modeled features, processed satellite imagery, ...) and as groundtruth/target variable a year per pixel (1867 ...
1
vote
0answers
13 views

Can regularisation reduce the accuracy in the validation test?

I am constructing a CNN neural network with TensorFlow. I have run two versions of the CNN, one of them without regularization and the other with a kernel regularizer $L^2$ in each convolutional layer....
0
votes
1answer
14 views

Best Practices in Sampling Large Datasets

I am working on a research paper concerning developing a CNN model for a multi-class classification on images. I have a large dataset consisting of 3 classes summing up to 20000 images. Class 1 has ...
1
vote
0answers
14 views

Weight regularisation in CNN

I am trying to understand the concept of weight regularisation in CNN. I know that in dense layer with weight $w$ it corresponds to finding: $$ \mathbf{w}^{*}=\underset{\mathbf{w}}{\arg \operatorname{...
1
vote
0answers
11 views

Creating a MRZ system on contracts

I'd like to create a MRZ detection system using a CNN : inputs are images of contracts and outputs are zones where to read. All the contracts have the same format but pictures can differ (light, angle....
1
vote
0answers
15 views

1D CNN for multistep multiclass timeseries classification

Suppose you have a timeseries classification task with n_classes possible classes, and you want to output the probability of each class for every timestep (like ...
1
vote
0answers
14 views

Why identity mapping is so hard for deeper neural network as suggested by Resnet paper?

In resnet paper they said that a deeper network should not produce more error than its shallow counterpart since it can learn the identity map for the extra added layer. But empirical result shown ...
1
vote
0answers
16 views

What kind of regularization can I use for CNN aside from L1/L2/Dropout? [closed]

I am building a CNN to estimate a sequence of pitches existed in a song with this architecture: ...
0
votes
0answers
62 views

What is the difference between FPN(Feature Pyramid Network), FPNlite and SSDlite?

I came across this when I used MobileNet v2 from tensorflow hub. I know that FPN means feature pyramid network and it's better at identifying smaller objects in the frame. However I still don't know ...
0
votes
0answers
11 views

Using GAN for image data augmentation (unbalanced dataset)

Lets say I have a image classification problem of 5 classes who are very similar to each other, the only difference is their length, and one class is under-represented. How can I use a GAN to create ...
1
vote
1answer
15 views

Can Neurons (Features) be repeated in Dense Layers

I was revising Convolutional Neural Networks and encountered the following question. If I were to classify a cat and a dog (the famous cats vs dog classifier), then assuming there are 2 Dense Layers ...
0
votes
0answers
13 views

How to decide which layer to use in a CNN for feature extraction?

Bear with me since I'm new to this stuff and a thousand google searches (and a dozen or so papers) wasn't able to answer my question. I'm working on my thesis which involves classification of blood ...
0
votes
0answers
30 views

Is CNN bad at recognizing a sequence of time-series data?

I am doing my final project at university: pitch estimation from song recording using convolutional neural network (CNN). I want to retrieve sequence of pitches which exist in a particular song ...
3
votes
1answer
16 views

Binary classification of each column in an image

I have a problem where I wish to classify each column in an image of a feature being present (1) or absent (0) in each column. The output of the model should be a vector of size of the width of an ...
0
votes
0answers
6 views

About the efficiency of MobileNet v1 architecture

The salient feature, which was said to make MobileNet v1 efficient in terms of computational complexity, is the usage of depthwise convolutions, which is in essence ...
0
votes
0answers
30 views

Creating an output image using Convolutional Neural Networks

I am currently working on undergraduate research to determine hotspots for hand-surface contact. Ideally, I would like to give the model a depth image as input: Example of a synthetic depth image and ...
0
votes
1answer
41 views

Inference Time in Neural Networks

Is the inference time directly proportional to both the number of operations in a network as well as the number of parameters? Or is it directly proportional to no. of operations and indirectly ...
0
votes
0answers
13 views

Padding images of size less than 5 by 5 pixels for CNN

I am working with multispectral Sentinel 2 images in 20m resolution meant as 3x3 pixel image is 60x60 m in extent in real. Most relevant information is in values of intensities of nine different bands....
0
votes
1answer
20 views

Would it be appropriate to compare knn and cnn algorithm for facial recognition?

I wanna compare these two algorithms but I'm not sure whether I should and also what parameters to keep in mind if I do ? what should I consider if I do go forward apart from the same data set of ...
0
votes
0answers
8 views

What activation should I use for an age estimation cnn?

New to CNNs and I am writing my first Keras CNN for age estimation and gender prediction. I am struggling to decide what activation I should use for the age output of my CNN. ...
1
vote
0answers
23 views

distribution of image data?

(I don't much about deep learning, but have been playing with a few things and have some questions.) I took the (pretrained on imagenet) resnet18 model from pytorch, removed the last fully-connected ...
0
votes
0answers
18 views

Resize mask after image segmentation

Context I trained a unet-like neural network for segmentation of specific objects. It takes as an input images with size 200x200. My input images are of shape 1000x1000, so I resize them with ...
0
votes
0answers
48 views

Back-propagation for 2D convolution using automatic differentiation

I am trying to learn about back propagation for convolutional neural networks by implementing automatic differentiation using numpy. I can understand the intuition behind a 2D convolution layer; ...
1
vote
1answer
71 views

CNN architecture for 1D time series classification

I would like to use a CNN in order to classify signal data consisting of min. 500 data points into 3 categories. What kind of architecture and design considerations do I need to take into account and ...
0
votes
0answers
20 views

Neural networks: general questions about training and hidden layers in LSTM

Studying neural networks, I have some questions and I cannot find answers online, so here I am: Since the candidate memory cells in LSTM are guaranteed by using the tanh function that the price range ...
2
votes
0answers
22 views

Confused over the shape of Feature Map

Taking for example a simple $(5,5,3)$ image where height=5, width=5, channels=3. If we define 2 filters of shape $(3,3,3)$ where each filter is of height=3, width=3, channels=3. We convolve these 2 ...
0
votes
0answers
17 views

How can I ensure that an autoencoder does not learn the mean?

I'm currently trying to reconstruct speech signals that are 3,000 samples long using an autoencoder in PyTorch. I currently have 90,000 examples of these speech signals to train on. Here is a summary ...
0
votes
0answers
9 views

When I train a Covolutional NN with weather data can I think about the layer of the network as an image with netcdf grid points instead of pixels?

I would like to quote a paragraph from (Nielsen, 2015): (Talkin about the CNN structure) [...] Local receptive fields: In the fully-connected layers shown earlier, the inputs were depicted as a ...
1
vote
1answer
18 views

Using 1d CNN for time series classification of patchy time series

I would like to apply 1d CNN like models for the patchy time series classification. For a LSTM like model, my plan to deal with the patchiness problem is to add a time column for the data. However, it ...
2
votes
1answer
15 views

It is always necessary to include a Flatten layer after a set of 2D convolutional layers for convolutional neural networks in Keras?

It is no clear for me when to use the flatten operation for building convnets. It is always necessary to include a flatten operation after a set of 2D convolutions (and pooling)? For example, let us ...
0
votes
0answers
18 views

Taking into account padding during backward pass for convolutional layers

I know there exists several threads on this matter but I could not find a satisfying answer so far. During backpropagation in a convolutional layer, we compute the gradient of the loss with respect to ...
1
vote
2answers
33 views

Why models often benefit from reducing the learning rate during training

In Keras official documentation for ReduceLROnPlateau class they mention that Models often benefit from reducing the learning rate Why is that so? It's counter-intuitive for me at least, since from ...
2
votes
0answers
71 views

LeNet-5 Subsample Layer in Tensorflow

In Tensorflow, how do you implement the LeNet-5 pooling layers with trainable coefficient and bias terms? Reading through the LeNet-5 paper, the subsample layers are described as follows: Layer S2 is ...

1
2 3 4 5
27