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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75 views

What happens with activations?

I am playing with convolution network, assembling something between AlexNet and ResNet. Not very deep, about 10 conv. layers including 2 through residual connection, and 3 fully-connected layes at the ...
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Dealing with low variation data

So my current project involves using a neural network to try and predict the probability of a player getting a kill in a first-person shooter. I've recorded a number of features that should be ...
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261 views

UNet Model accuracy is stuck at exact 0.5 (neither more or less) (No class imbalance, tried tuning learning rate)

This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Loss does decrease. I have also checked for class imbalance. I have ...
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What does “full connection table” mean in Yan LeCuns comment on 1x1 convolutions?

What does "full connection table" mean in Yan LeCuns comment on 1x1 convolutions? In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with ...
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Preparing text for modeling in dialogue structure

I'm working on implementing the DialogueGCN code from this paper. Its a model that classifies the 'emotion' from utterances of text within a conversation. As this model takes into account speaker ...
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Keras CNN model gives no gradients error during training

I’m trying to create a Convolutional Neural Network model, using an 824 image dataset, for predicting an output value. Problem is that the dataset is quite unstructured, as there are plenty of RGB and ...
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262 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
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Best model structure for input coordinates producing full images

I am trying to produce a machine learning model that works as an interpolator of real data. Essentially what would go into the model is an xy coordinate the result ...
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The idea behind Generalized Max Pooling

I am trying to understand the idea of "Generalized Max Pooling". It seems they try to make the 'pooled' representation similar to the features. If so I feel some rare discriminating features could ...
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What is the meaning of 'concatenate' in this neural network architecture?

I am trying to understand the lane edge proposal network proposed in LaneNet for lane detection. My understanding of this is that a number of convolutional and pooling layers are first used to ...
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Using a 3-D convolutional layer to simulate a 2-D convolutional layer

I've asked this question on the AI StackExchange , but I received no insight so I'd like to ask it here. Is using a filter of size (1, x, y) on a 3-D convolutional layer the functionally equivalent ...
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Gender Prediction from Offline Handwriting Using Convolutional Neural Networks

Starting from the fact that handwritten documents style are gender-dependent (male and female have different writing styles), I'm trying to predict writer's gender from its handwritten scripts using ...
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1D convolutional neural network validation improvement

I created 1D CNN in Keras, but I'm having issues with validation loss and accuracy. I have 24k records, 22 features. Is my model overfitting or what is going on so validation loss and accuracy is ...
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Back propagation through a simple convolutional neural network

Hi I am working on a simple convolution neural network (image attached below). The input image is 5x5, the kernel is 2x2 and it undergoes a ReLU activation function. After ReLU it gets max pooled by a ...
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Is always converting a input vector into matrix and apply cnn's good idea?

I know the benefits of using cnns(reduced size weight matrices). Is it a good idea to convert a input vector(which is not a image) into a matrix and apply cnn's. What I understand is that it should ...
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1answer
203 views

pytorch convolution with 0-stride along one dimension

For some square images, I'd like to use torch.nn.Conv2d with the kernel as a vertical block. As in, the kernel size is defined as max value of the first dimension by 1. Since the first dimension has ...
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107 views

Plotting scikit-learn confusion matrix returns no values in the last class

I am attempting to create a confusion matrix using Scikit-Learn for a multiclass classification CNN, and it works well except for the fact that it does not provide ...
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ConvNet - What to improve regarding architecture, procedure and technique?

I have a dataset of 180k images of license plates (so, not necessary to localize the license plate at first) for which I try to recognize the characters on the images (License plate recognition). All ...
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Why are results without Transfer Learning better than with Transfer Learning?

I developed a neural network for license plate recognition and used the EfficientNet architecture (https://keras.io/api/applications/efficientnet/#efficientnetb0-function) with and without pretrained ...
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Understanding declared parameters in my Conv2d layer of my convolutional neural network

I am trying to understand the architecture of my keras model implemented by the sequential model. Here is a piece of the code : ...
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2answers
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Is Flatten() layer in keras necessary?

In CNN transfer learning, after applying convolution and pooling,is Flatten() layer necessary? I have seen an example where after removing top layer of a vgg16 ,first applied layer was ...
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1answer
405 views

PyTorchs ConvTranspose2d padding parameter

Im confused about what PyTorchs padding parameter does when using torch.nn.ConvTranspose2d. The docs say that: "The padding argument effectively adds dilation * (kernel_size - 1) - padding ...
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1answer
30 views

EfficientNet function composition or Hadamard

In the page 3 of the paper of EfficientNet, there is a equation $$\mathcal{N} = \bigodot_{i=1...s} \mathcal{F}_{i}^{L_i} \big(X_{\langle H_i, W_i, C_i \rangle}\big)$$ where $\mathcal{N}$ is the conv ...
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Can we use CNN to effectively learn from a table whose various rows are permuted in the testing dataset, output for each remains same?

I have a set of classes, 37 to be precise. Each class is represented by a feature vector of size [1,10]. A single input sample has the dimensions of ...
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Understanding the network structure of a multiple timeseries fusion model

Please don't mark this question as duplicated to Can I create a layer with multiple rnn cell ? [question about a paper] It has already been marked 2 times , I admit they do refer to a same paper, but ...
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How to build a classification pipeline that will pass to another model?

Not sure if the title explained it, but I am trying to build a pipeline where it's like a decision tree, but also not. Say for example, I had a picture. The model classified the picture, but now I ...
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Do smaller neural nets always converge faster than larger ones?

In your experience, do smaller CNN models (fewer params) converge faster than larger models? I would think yes, naturally, because there are fewer parameters to optimize. However, I am training a a ...
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About the relevance and interprertability of convolutional filters?

Convolution filters are known to perform very well in tasks, concerning some work with the image or video data, due to their ability to preserve some spatial information and equivariance property ...
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Theoretically Speaking, How Do Squeeze-and-Excitation Blocks Help?

A SE block works by assigning a weight to each channel, contrary to a vanilla filter, which gives equal importance to all channels. My question is, theoretically speaking, shouldn't a regular filter ...
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1answer
854 views

How to Connect Convolutional layer to Fully Connected layer in Pytorch while Implementing SRGAN

I was implementing the SRGAN in PyTorch but while implementing the discriminator I was confused about how to add a fully connected layer of 1024 units after the final convolutional layer My input ...
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What are the activation functions in Convolutional Layers for?

I read a lot about CNNs but I didn't quite understand some things: What are the activation function in CLayers for? If I understood it right, the only weights in these layers are the ones in Filters, ...

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