Questions tagged [convolutional-neural-networks]

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

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When is an object detection approach over a CNN approach appropriate?

I understand that CNNs are for image classification while object detection is for localization + classification of the objects detected. However, in particular AI for chest radiographs, why is object ...
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pytorch TypeError: forward() takes 1 positional argument but 2 were given

I have been trying to implement a small VGG network but run into this error. Here is the error message I am getting: ...
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1answer
32 views

Is there any recommended way to perform pooling in this context?

Suppose I have three batches of feature maps, each of size $180 \times 100 \times 100$. I want to concatenate all these feature maps channel-wise, and then resize them into a single feature map. The ...
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Improving validation losses and accuracy for 3D CNN

I have used a 3D CNN architecture, for detecting the presence of a particular promoter (MGMT), by using FLAIR brain scans. (64 slices per patient). The output is supposed to be binary (0/1). I have ...
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1answer
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How do I choose the hyper-parameters for a model to detect different guitar chords?

I need to build a hand detector that recognizes the chord played by a hand on a guitar. I read this article Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation ...
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What is meant by "spatial encoding" in the context of convolutional neural networks?

Consider the following excerpt from the abstract of the research paper titled Squeeze-and-Excitation networks by Jie Hu et al. Convolutional neural networks are built upon the convolution operation, ...
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What do people refer to when they use the word 'dimensionality' in the context of convolutional layer?

In practical applications, we generally talk about three types of convolution layers: 1-dimensional convolution, 2-dimensional convolution, and 3-dimensional convolution. Most popular packages like ...
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When to use Multi-class CNN vs. one-class CNN

I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN. That is, if I'm making e.g. a ...
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What practically makes a good architecture of ANN?

When we take a look at the literature there are so many opinions. I was wondering what are some generally good practices to design an architecture, like how much depth would you prefer and how much ...
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1answer
26 views

Is it possible to have different channel dimensions in a CNN?

Let's say I have two channels that I wish to feed into a CNN. One of the channel contains 4 traces and has a width of 512. Stacking them on top of each other therefore yields an image with dimensions (...
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47 views

Correctly input additional values into CNN

I understand that in order to add additional inputs to a CNN, e.g. in self driving, I can append the data to a flattened layer after the convolutions and before the fully connected layers. However, a ...
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1answer
28 views

How do you handle unbalanced image datasets?

I have an image data set on which I am training a CNN. The data set is slightly unbalanced. So, my solution up till now was to delete some images of the majority class. But I now realize that there ...
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59 views

How general is generalization?

I am sorry but I have to explain my question using an example, I do not know how to ask it in proper scientific terms. Let's assume, I have trained a deep learning model on classifying hand gestures, ...
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1answer
30 views

Is it true that channels always represent colours of an image?

Convolutional neural networks are widely used in image-related tasks in artificial intelligence. The input of a conventional neural network is generally an image. The output of a convolutional neural ...
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Brain tumour detection using CNN

I have a fairly basic mathematical and implementational understanding of ML algorithms and CNNs, and I am trying to think of an approach for this task: https://www.kaggle.com/c/rsna-miccai-brain-tumor-...
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Counting number of coaches in a train from real time video feed

I have a real time video feed of a train platform. I was able to detect coaches using CNN based model. But how can I calculate number of coaches in the train that passed the platform as well as the ...
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1answer
27 views

What is meant by "shorter connections" in the case of deep convolutional neural networks?

Consider the following two excerpts from the research paper titled Densely Connected Convolutional Networks by Gao Huang et al. #1: From abstract Recent work has shown that convolutional networks can ...
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How to Train Big Size Image and Predict Various Size of Images

I don't have deep knowledge of the neural network, but I would like to segment the road from UAV images and detect cracks on them. My first question: I am planning to do fine-tuning from pre-trained ...
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8 views

Loss & accuracy curves from learning rate range test interpretation

I am working on a project doing experiments with the Learning Rate Range Test (See "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and ...
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1answer
68 views

Why is the validation accuracy lower in case of CNN?

I fed the same set of 1.4 million data to two different models: MLP CNN model In both cases, I used the same parameters and hyperparameters. The CNN is showing comparatively lower accuracy (80%) ...
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47 views

When should we use CNN instead of MLP?

Is CNN only applicable to time-series data or image data? When should we use CNN instead of MLP?
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38 views

Is it possible to apply 2D convolution to 1D data?

Suppose that I have a 1D dataset with 6 features. Can I apply a 2D convolutional neural net to this dataset?
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10 views

Continue teaching pre-trained network without forgetting previous data set

I have a rather interesting problem here; I work in the field of image classification for quality assurance. For this I have a dataset of about 1 million images, which I have used to train different ...
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What does it mean by "low-level" and "high-level" in features generated by CNN?

Across the literature, the terms "high-level" and "low-level" are generally used as an adjective to the features generated by the convolution neural network. Should I understand ...
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How does bipartite matching work in DETR?

I was going through the DETR paper to understand this end-to-end detection transformer used for object detection, and I came across this bipartite matching thing.
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1answer
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Validation accuracy less than training accuracy (with no sigh of overtraining)

I am working with a deep CNN with over 100k sample data. I divided it up into 75% training, 12.5% validation and 12.5% for testing. As I train my network, the training accuracy approaches near 100% ...
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36 views

How does Chebyshev approximation of spectral convolution work?

I was reading the following paper: here. In it, it talks about spectral graph convolutions and says: We consider spectral convolutions on graphs defined as the multiplication of a signal $x \in R^N$ (...
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Is it normal that we get different AUC results after running with various seeds?

We are working on optimizing a CNN made for binary image classification (by that I mean to classify each image to group A or group B). It is based on InceptionV3, using PyTorch. We saw that choosing ...
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1answer
59 views

How do I prepare my data for a CNN to be applied to a geophysical-related problem?

I am currently doing research work on an inversion of geophysical data using Machine Learning. I have come across some research work where a Convolutional Neural Network (CNN) has been used ...
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Is having near-duplicates in a training dataset a bad thing?

I am making a labeled dataset of images from web streams for a CNN classification. Pictures from the same stream are quite similar as far as background, but slightly different as far as the main ...
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1answer
20 views

How to embed game grid state with walls as an input to neural network

I've read most of the posts on here regarding this subject, however most of them deal with gameboards where there are two different categories of single pieces on a board without walls etc. My game ...
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Dealing with images of multivariate time series

Assuming we have the following input multivariate series: number_of_samples, number_of_timestamps, number_of_features Upon conversion to images using any of the ...
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Preprocessing images for test and validation datasets for training a convolutional neural network (CLAHE)?

I'm training a convolutional neural network for image classification,and i want to preprocess the images, for example with the CLAHE method. I'm not sure if this preprocessing has to be used on the ...
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1answer
30 views

How to divide a segmented image into classes instances?

Is there a method/algorithm to generate instances of objects from image that was segmented by the use of any image segmentation models? For example, I have an image with one class and it was segmented ...
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26 views

Machine Learning Algorithm for OCR on full pages of text

I would like to build an OCR application. In. particular, I want my algorithm to scan entire pages of text in a specific niche language. I was therefore wondering if there are some algorithms that ...
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44 views

How do CNNs handle inputs of different sizes and shapes?

I am new to deep learning so feel free to correct me where I am wrong. Imagine this scenario where we have a 7 * 7 input. We want to slide a 3 * 3 filter with a stride of 3 and padding of zero over ...
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1answer
48 views

Why do some techniques use random augmentations during convolution processes

While going over PyTorch image augmentations, https://pytorch.org/vision/stable/transforms.html, I see that some augmentations can be applied with a certain probability. What is the purpose of ...
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Is it possible to identify which feature maps were generated from a particular image after convolutional operation

Let's say I have a video that contains 3 grayscale sequential frames having a combined shape of (3, 24, 24). After inputting these frames together into a CNN, multiple feature maps will be generated ...
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1answer
26 views

How does a VGG-based Style-Loss incorporate color information?

I've recently been reading a lot about style transfer, its applications and implications. I understand what the Gram matrix is and does. I can program it. But one thing that has been boggling me is: ...
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19 views

Which Neural Network Topology to choose, are Transformers suitable?

I have a regression problem and I am not quite sure which architecture to choose. I never worked with transformers before, but I generally understand how they work and I think they might be suitable. ...
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1answer
85 views

What is the difference between a vision transformer and image based relational learning

I am trying to figure out the difference between the architecture used in this and this paper. It looks like both used multi-headed self-attention and therefore should be the same in principle.
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What does a value of -1.000 mean in MS COCO Metrics for Object Detection

I am training some Object-Detection-Models from the TensorFlow Object Detection API and got from the evaluation with MS COCO metrics the following results for Average Precision: IoU = 0.5;0.9 maxDets =...
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How can equivariance to translation be a benefit of a CNN?

I just learnt about the properties of equivariance and invariance to translation and other transformations. Being invariant to translation is clearly an advantage, as even if the input gets shifted, ...
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31 views

LSTM and CNN - feature engineering and order for time series classification

My questions are related to multivariate time series classification, hence it may differ from forecasting problems. I can have either variable (entire history of the series) or fixed time steps (...
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1answer
59 views

Why the partial derivative is $0$ when $F_{ij}^l < 0$?. Math behind style transfer

I am currently in the process of reading and understanding the process of style transfer. I came across this equation in the research paper which went like - For context, here is the paragraph - ...
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What would be the advantage of making channel dimension first in TensorFlow Keras implementation?

I was reproducing the findings of a research article in which I discovered that they had switched the Channel dimension from last to first. To clarify this concept, I went through A Gentle ...
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1answer
34 views

Given an input of shape $(3, 32, 32)$, which is convolved with a $(3 \times 3)$ kernel, how do I calculate the FLOPS?

I have an input tensor of shape $\mathbf{(3, 32, 32)}$ consisting of 3 channels, 16 rows, and 16 columns. I want to convolve the input tensor using $\mathbf{(3 \times 3)}$ kernel/filter. How can I ...
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How to pass variable length data as feature to a neural network?

I am working on building a model to classify the type of touch the user makes(Long Press, Left Swipe, Right swipe and so on). I have data with features that characterise the user's touch, like ...
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What is the significance behind having small kernel sizes over having one large kernel size that covers the entire input in a CNN?

I have hardly ever seen anyone cover the entire input image with a filter of the same dimensions. I was wondering why that is the case, and if the performance in say, an image detection application ...

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