Questions tagged [convolutional-neural-networks]

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

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Predicting continous value with CNN (prediction of fruit maturity)

I want to train some IA algorithm to be able to evaluate the maturity of a fruit (say, measured in numbers of days before rotten) based on an image of the fruit. My first instinct is to go with ...
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8answers
37k views

In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?

My understanding is that the convolutional layer of a convolutional neural network has four dimensions: input_channels, filter_height, filter_width, number_of_filters. Furthermore, it is my ...
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1answer
103 views

Why is my fine-tuned YOLO model detecting other objects as a human?

I am new to deep learning and computer vision. I have a problem where I use the YOLO to detect objects. For my problem, I just want to recognize 1 human only. So, I changed the final YOLO's layer (...
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4answers
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What are examples of books or papers on the details of convolutional neural networks?

I'm studying a master's degree and my final work is going to be about the convolutional neural network. I read a lot of books and I did Convolutional Network Standford's course, but I need more. Are ...
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Keras 1D CNN always predicts the same result even if accuracy is high on training set

The validation accuracy of my 1D CNN is stuck on 0.5 and that's because I'm always getting the same prediction out of a balanced data set. At the same time my training accuracy keeps increasing and ...
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1answer
106 views

If vanishing gradients are NOT the problem that ResNets solve, then what is the explanation behind ResNet success?

I often see blog posts or questions on here starting with the premise that ResNets solve the vanishing gradient problem. The original 2015 paper contains the following passage in section 4.1: We ...
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1answer
30 views

What happens if there is no activation function in some layers of a neural network?

What if I don't apply an activation function on some layers in a neural network. How will it affect the model? Take for instance the following code snippet: ...
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0answers
71 views

Given the coordinates of an object in an image, is it possible to predict the coordinates of the same object in a different perspective?

I am trying to figure out how to approach this. Given training data of images and the pixel coordinates of the centre of an object in that image, would it be possible to predict the pixel coordinates ...
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15 views

How to get the prediction probability of random sample image from multiclass classification model?

I am performing classification using AlexNet as transfer learning(simply say performing classification using CNN) for five types of class on 18000 images. These 18000 images are divided into Train, ...
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2answers
47 views

Convolutional Sequence to Sequence Learning kernel parameters

I am reading the paper Convolutional Sequence to Sequence Learning by Facebook AI researchers and having trouble to understand how the dimensions of convolutional filters work here. Please take a look ...
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1answer
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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
34 views

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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38 views

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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1answer
36 views

How can I implement 2D CNN filter with channelwise-bound kernel weights?

I would like to bind kernel parameters through channels/feature-maps for each filter. In a conv2d operation, each filter consists of HxWxC parameters I would like to have filters that have HxW ...
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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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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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4answers
368 views

Is the pattern recognition capability of CNNs limited to image processing?

Can a Convolutional Neural Network be used for pattern recognition in problem domains without image data? For example, by representing abstract data in an image-like format with spatial relations? ...
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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
54 views

Is it possible to vectorise a CNN?

I am trying to write a CNN from scratch and am wondering if it is possible to vectorize the convolution step. For example, if I had a dataset of 500 RGB images of size 32x32x3, and wanted the first ...
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1answer
50 views

How can max-pooling be applied to find features in words?

I'm reading about max-pooling in a dynamic CNN paper. I can see how it can help find features in images, given that the pixel with the highest density gets pooled, but how does it help to find ...
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1answer
45 views

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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1answer
51 views

Do all filters of the same convolutional layer need to have the same dimensions and stride?

In Convolutional Neural Networks, do all filters of the same convolutional layer need to have the same dimensions and stride? If they don't, then it would seem the channel produced by each filter ...
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2answers
18 views

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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2answers
359 views

What do the terms “front-end” and “back-end” refer to in this article?

I found the terms front-end and back-end in the article (or blog post) How to Develop a CNN for MNIST Handwritten Digit Classification. What do they mean here? Are these terms standard in this context?...
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1answer
62 views

Relationship between input range and channel means, standard deviations for CNNs

So, I'm using a pretrained pnasnet5large model to do some image classification (https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py) In the file, it ...
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1answer
169 views

When doing transfer learning, which initial layers do we need to freeze, and how should I change the last layer for my task?

I want to train a neural network for the detection of a single class, but I will be extending it to detect more classes. To solve this task, I selected the PyTorch framework. I came across transfer ...
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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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0answers
20 views

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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20 views

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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0answers
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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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2answers
1k views

What is the exact output of the Inception ResNet V2's feature extraction layer?

I am working with the Inception ResNet V2 model, pre-trained with ImageNet, for face recognition. However, I'm so confused about what the exact output of the feature extraction layer (i.e. the layer ...
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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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634 views

Applying a 1D convolution for 4D input

i'm trying to implement this paper and I'm stuck for quite some time now. Here is the issue: I have a 3D tensor and has (180,200,20) as dimension and I'm trying ...
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13 views

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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1answer
238 views

Which neural networks are suitable for visual place recognition?

I am doing a project on visual place recognition in changing environments. The CNN used here is mostly AlexNet, and a feature vector is constructed from layer 3. Does anyone know of similar work ...
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18 views

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
49 views

In CNNs, why do we sum the filter derivatives w.r.t the loss function to get the final gradient?

In a Convolutional Neural Network, unlike the fully connected layers, the same filter is used multiple times on the input while convolving - so during backpropagation, we get multiple derivatives for ...
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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 ...
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1answer
39 views

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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1answer
83 views

How to handle extremely 'long' images?

After transforming timeseries into an image format, I get a width-height ratio of ~135. Typical image CNN applications involve either square or reasonably-rectangular proportions - whereas mine look ...
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2answers
71 views

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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0answers
13 views

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
36 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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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
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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1answer
45 views

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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1answer
41 views

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
71 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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16 views

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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1answer
115 views

How is the depth of filters of hidden layers determined?

I am a bit confused about the depth of the convolutional filters in a CNN. At layer 1, there are usually about 40 3x3x3 filters. Each of these filters outputs a 2d array, so the total output of the ...

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