Questions tagged [convolutional-neural-networks]

For questions about convolutional neural networks, also known as CNN or ConvNet.

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31 views

Is it possible to train one part of the network with a particular learning rate and the other part with a different one?

I have a combined network consisting of two parts: one is for images and the other is for numerical data. Each sample is matched with a numerical case by an ID. For this combined network, a ...
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1answer
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Can a convolutional network predict states for a RL Agent

During the course of training a DQN agent, all visited states are stored in a replay buffer. Therefore would it be practically possible for a CNN, given a reasonable amount of data, to predict the ...
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How to extract parameters from a text using AI/NLP

lets say I have three texts: "make a heading that says hello word" "make a heading of hello world" "create heading consist of hello world" How can I fetch those groups ...
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Graph Neural Networks: Quesitons about different GCN Architectures

This might be moreof a question about nested function classes: For k class node classification in a graph with n nodes, and d feature vector. I want to compare Architecture I: the GCN model of Kipf/ ...
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looking for EfficientDet weights pretrained on Open Images [closed]

I've been looking for EfficientDet weights pretrained on Open Images or some other huge data set. However, I was only able to find weights pretrained on COCO. Are you aware of any repository that ...
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1answer
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How do you pass the image from one convolutional layer to another in a CNN?

I am currently trying to write a CNN from scratch, but I don't understand how to feed the information from a max-pooling layer to the next convolutional layer. Specifically, I don't know what to do ...
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In the DeepView paper, do they use the same FCN for all depth slices AND all views?

I'm trying to replicate a paper from Google on view synthesis/lightfields from 2019: DeepView: View Synthesis with Learned Gradient Descent and this is the PDF. Basically the input to the neural ...
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Semantic segmentation CNN outputs all zeroes

I'm using MATLAB 2019, Linux, and UNet (a CNN specifically designed for semantic segmentation). I'm training the network to classify all pixels in an image as either cell or background to get ...
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1answer
53 views

What is the use of the regular convolutional layer in expansion path of U-Net?

I was going through the paper on U-Net. U-net consists of a contracting path followed by an expanding path. Both the paths use a regular convolutional layer. I understand the use of convolutional ...
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How do gradients are flown back into the Siamese network when branching is done?

I am curious about the working of a Siamese network. So, let us suppose I am using a triplet loss for my network and I have instantiated single CNN 3 times and there are 3 inputs to the network. So, ...
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Jetson Nano low CNN inference performance [migrated]

I'm running what I believe is a pretty small CNN on an nVidia Jetson Nano with Jetpack 4.4. nVidia claims the Nano can run a ResNet-50 at 36fps, so I expected my much smaller network to run at 30+ fps ...
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What are the pros and cons of 3D CNN and 2D CNN combined with optical flow for action recognition?

For action recognition or similar tasks, one can either use 3D CNN or combine 2D CNN with optical flow. See this paper for details. Can someone tell the pros/cons of each, in terms of accuracy, cost ...
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Is there any research work that shows that we should explicitly mark the word boundaries for 1D CNNs?

I'm doing character embedding for NLP tasks using one-dimensional convolutional neural networks (see Chiu and Nichols (2016) for the motivation). I haven't found any empirical evidence of whether or ...
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1answer
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How is the latent vector transforming to a feature map in DCGAN (Generator structure)?

I'm working on the code trying to generate new images using DCGAN model. The structure of my code is from the PyTorch tutorial here. I'm a bit confused trying to find and understand how the latent ...
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Considerations when doing image classification where the object is not the subject

I've come across two types of image classification tasks cat/dog classification the whole picture is either a cat or a dog. Simple. this image contains a cat classification. There's a whole chaotic ...
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1answer
35 views

Output volume proof for convolutional neural network

As I've been dabbling into the sliding window concept, I stumbled on a question that asked me to find the number of windows needed on a 1D image of $W$ size, knowing the window size $K$ and the stride ...
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2answers
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Is there anything that ensures that convolutional filters end up different from one another?

I found this question very interesting, and this is a follow up on it. Presumably, we'd want all the filters to converge towards some complementary set, where each filter fills as large a niche as ...
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How to build a commercial Image-Image search engine using LSH / Near Duplicate or some other algo on more than 20M images

TL;DR: HOW DO I APPLY LSH WITH A DEEP LEARNING MODEL TO BUILD A IMAGE-IMAGE SEARCH ENGINE ON >20M IMAGES? I want to build a system where I am helping my ...
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Do filters have as many layers as the depth of the input in CNNs? [duplicate]

Firstly as an example here is the architecture of YOLOv2 I am trying to understand the depth of an output of a convolutional layer. For example, the first convolutional layer has the shape 3x3x32. So ...
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How will the filter size affect the transpose convolution operation?

After a series of convolutions, I am up-sampling a compressed representation, I was curious what is the methodology I should follow to choose an optimum kernel size for up-sampling. How will the ...
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Are Graph Neural Networks generalizations of Convolutional Neural Networks?

In lecture 4 of this course, the instructor argues that GNNs are generalizations of CNNs, and that one can recover CNNs from GNNs. He presents the following diagram (on the right) and mentions that it ...
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2answers
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Appropriate convolutional neural network architecture when the input consists of two distinct signals

I have a dataset consisting of a set of samples. Each sample consists of two distinct desctized signals S1(t), S2(t). Both signals are synchronous; however, they show different aspects of a phenomena. ...
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What is the effect of too harsh regularization?

While training a CNN model, I used an l1_l2 regularization (i.e. I applied both $L_1$ and $L_2$ regularization) on the final layers. While training, I saw the ...
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1answer
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How can I do video classification while taking into account the temporal dependencies of the frames?

I need to solve a video classification problem. While looking for solutions, I only found solutions that transform this problem into a series of simpler image classification tasks. However, this ...
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1answer
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What's the difference between architectures and backbones?

In the paper "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", the authors talk about using: Feature Pyramid Networks (as the ...
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Is there any rule of thumb to determine the amount of data needed to train a CNN

I am training an AlexNet Convolutional Neural Network to classify images in a dataset. I want to know if there is any general rule for using data augmentation in training a neural network. How can I ...
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1answer
122 views

Can I start with perfect discriminator in GAN?

In many implementations/tutorials of GANs that I've seen so far (e.g. this), the generator and discriminator start with no prior knowledge. They continuously improve their performance with training. ...
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1answer
52 views

Can someone explain me what does this loss curve says?

I was training a CNN model on TensorFlow. After a while I came back and saw this loss curve: The green curve is training loss and the gray one is validation loss. I know that before epoch 394 the ...
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Why shouldn't batch normalisation layers be learnable during fine-tuning?

I have been reading this TensorFlow tutorial on transfer learning, where they unfroze the whole model and then they say: When you unfreeze a model that contains ...
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How do I infer exploding or vanishing gradients in Keras?

It may already be obvious that I am just a practitioner and just a beginner to Deep Learning. I am still figuring out lots of "WHY"s and "HOW"s of DL. So, for example, if I train a ...
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1answer
59 views

Can a convolutional neural network classify text document images?

I know convolutional neural networks are commonly used for image recognition, but I was wondering if they would be able to distinguish between predominantly text-based documents vs something like ...
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1answer
63 views

Is there anything that ensures that convolutional filters don't end up the same?

I trained a simple model to recognize handwritten numbers from the mnist dataset. Here it is: ...
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1answer
40 views

What is the difference between Attention Gate and CNN filters?

Attention models/gates are used to focus/pay attention to the important regions. According to this paper, the authors describe that a model with Attention Gate (AG) can be trained from scratch. Then ...
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1answer
36 views

How to use a conv2d layer after a flatten?

I am not familiar with Deep learning and Pytorch. And I want to know how to deal, in general with such a situation. So, I was wondering if I used a pretrained model (EfficientNet for example) if I ...
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24 views

Time series prediction using LSTM and CNN-LSTM: which is better?

I am working on LSTM and CNN to solve the time series prediction problem. I have seen some tutorial examples of time series prediction using CNN-LSTM. But I don't know if it is better than what I ...
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37 views

How to edit a photo using deep learning?

I just took a course on deep learning where one part of the syllabus was image classification and object recognition using CNNs, but I wonder how deep learning can be applied to apply certain filters ...
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1answer
23 views

Training a model to identify certain differences between images?

Newbie to CV here so sorry of this is basic. Here's the deal, I have a program that I run many times. and each run I produce a screenshot. I need to compare screenshots from N-1 and N runs and make ...
2
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1answer
44 views

Explanation of this L2 minimization equation

I am trying to understand the last two lines of this math notation. How Var and double summation of Cov came to the equation. The first two lines I understood something like $(a-b)^2 = a^2 -2ab +b^2$.
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2answers
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Are convolutional neural networks inspired by the human brain?

The Deep Learning book by Goodfellow et al. states Convolutional networks stand out as an example of neuroscientific principles influencing deep learning. Are convolutional neural networks (CNNs) ...
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0answers
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Image classification - Need method to classify “unknown” objects as “trash” (3D objects)

We have an image classifier that was built using CNN with faster R-CNN and Yolov5. It is designated to run on 3D objects. All of those objects have similar "features" structure, but the ...
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0answers
14 views

Add Additional Positional Information to Image Classification Neural Network

I am trying to find the best way to provide a neural network with both an image and some annotations about the image. Specifically, I'm creating a network to calculate an approximate 'cost' to go from ...
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1answer
70 views

How can we derive a Convolution Neural Network from a more generic Graph Neural Network?

Convolution Neural Network (CNNs) operate over strict grid-like structures ($M \times N \times C$ images), whereas Graph Neural Networks (GNNs) can operate over all-flexible graphs, with an undefined ...
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Why does the validation loss not go down when training my TensorFlow implementation of AlexNet?

I am trying to implement AlexNet in Tensorflow. Matlab has a built-in AlexNet implementation that I use with the cats and dogs dataset (mledu-datasets/cats_and_dogs_filtered.zip). In Matlab, ...
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29 views

How to generate a matrix out of sparse data?

I have a system that takes 32 inputs (all of which are 1 or 0) and generates 32 outputs (all of which are complex numbers that lie roughly in the range of (0,2)). The response of this system to its ...
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1answer
28 views

Semantic segmentation failing in small instance detection

I performed semantic segmentation with U-net. My dataset consists of grayscale images of defects. After training the dataset for I got an metric accuracy of only 0.3 - 0.4 IOU. Eventhough it is merely ...
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How to find rotation x/y/z of chessboard diagram with what network architecture?

I want to recognize pieces of chessboard diagram (not real 3d pieces but just diagram). I split this task in some operation like rotation/cutting/segmentation. First of all I want to detect chessboard ...
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1answer
59 views

Can the (sparse) categorical cross-entropy be greater than one?

I am using AlexNet CNN to classify my dataset which contains 10 classes and 1000 data for each class, with 60-30-10, splits for train, validation, and test. I used different batch sizes, learning ...
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0answers
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What are the benefits of using ELU over other activation functions in CNNs?

I have come up with some examples of CNNs (segmentation CNNs) that use ELU (exponential linear unit) as an activation function. What are the benefits of this activation function over others, such as ...
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1answer
86 views

Difference between Neural Compute Stick 2 and Google Coral USB for edge computing [closed]

I am trying understand machine learning inferece, and i would like to know what exactly is the difference between Google Coral USB and Movidius Intel Neural Compute Stick 2. From what i could gather ...
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1answer
60 views

Keras DQN Model with Multiple Inputs and Multiple Outputs [closed]

I am trying to create a DQN agent where I have 2 inputs: the agent's position and a matrix of 0s and 1s. The output is composed of the agent's new chosen position, a matrix of 0s and 1s (different ...

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