Questions tagged [convolutional-neural-networks]

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

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Can we use 3D convolution to do 2D convolution on list of input?

I am trying to run 2D convolution on a set of input tensor. The input dimension would be [batch x channel x N x height x width], where N is the number of input matrices. I want to apply 2D convolution ...
ryan chandra's user avatar
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Wandb Sweep not working

The problem is that, I was trying to perform hyper-parameter sweep using wandb, the first sweep runs for set no. of epochs, but the consecutive sweeps just run for 1 epoch. For proof I attach the ...
Sarvagya Porwal's user avatar
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1D CNN with Single vs. Two Channels for Number Image Recognition

I am taking images of numbers as input, in a convolutional neural network and building a model to predict the number. In particular, I am building a one dimensional convolutional neural network with ...
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Must a CNN (both 1D and 2D) take input of the same size?

I have the notion that CNN input data must always be of the same dimensions. If we are feeding 1D tabular data, columns must be of the same numbers; if we are feeding 2D image data, all the images ...
user366312's user avatar
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Do neural networks have a perception of space, regardless of dimensionality?

Suppose I have a model M which outputs a three-dimensional tensor of size 3x3x3. I have another model N which outputs a one-dimensional tensor of size 27. Train both models on some arbitrary objective ...
schmibbler's user avatar
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classification od crops based on satellite imagery [closed]

hi i was wondering if it is possible to classify the crops based on the high quality satellite images which are in the form of tif format. how do we even classify the crops(like we first go form the ...
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weighted multi class classification

i'm working on a multi class classification problem which classifies jellyfish and plastic pollution so basically i have 6 classes (barrel_jellyfish, compass_jellyfish, lions_mane_jellyfish, ...
Gabovix's user avatar
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How to remove random noise from an image (denoising)?

When adding noise to an image, for instance, is the noise added evenly random (equally likely values within some range), or random but following some distribution (like the normal distribution)? Then,...
James's user avatar
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How to perform latent space Interpolation between two images?

I have a variational convolutional autoencoder that has trained on 2 images and outputs a linear interpolation (inserted at the bottleneck stage) between those 2 input images. However, the result ...
James's user avatar
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CNN multioutput regression architecture modification

I am working on a regression task where the model has to predict two values at the same time. The idea is that the dataset consists of 16 features, where the first 8 features represent the first value ...
lukachu03's user avatar
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How to reconstruct a new image using pre-trained autoencoder?

When a single image is assigned for training, an auto-encoder should be able to gradient-descend and find the full set of satisfactory weights that will reconstruct this image. Suppose a second image ...
James's user avatar
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Is there any standardized notation for drawing neural network diagrams?

Is there any standardized notation for drawing neural network diagrams? For example, for circuits there is a universal set of symbols used to draw different types of circuits why not for neural ...
play's user avatar
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CNN Input shape for DQN Q-calculating Network

Context: I want to build a DQN with as CNN for calculating its Q value on each step. Enviroment's status can be described by the attributes of 3 machines (each one with own attributes). I'd also like ...
Oliver Mohr Bonometti's user avatar
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Trained model on cifar10 performs poorly on real images

So I'm trying to train a model using the CIFAR10 dataset. The problem is that while the performance of the model on validation and test sets are good (about 95-96%), the model fails to predict images ...
AlbertDang's user avatar
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Why different noise in GAN generate different images?

I understand that noise $z$ serves as the input to the generator. Noise $z$ is essentially a vector of random numbers, typically from Gaussian distribution with chosen size of like $100$. However, I ...
abcd's user avatar
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How do transformer-based architectures generate contextual embeddings?

How do transformer-based architectures like Roberta generate contextual embeddings? The articles I've read keep saying that transformer encoders work bidirectionally. Because of self-attention, they ...
abcd's user avatar
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How can I process financial stock data before passing it to an LSTM for time series classification?

I'm trying to make an LSTM that can classify the next day of a stock as either 1 or 0 for going up or down. The issue I've been having is that Keras tuner seems to stay at a constant value of val_loss ...
JhonDenver's user avatar
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How to fine-tune pre-trained model? [closed]

I'm trying to classify a data set of medical images with a pre-trained model EfficientNetB0. I've written a code in Python with Pytorch to train my model and fine-tune it but I would like to know if ...
NitaStack's user avatar
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Extracting features from multiple curves

I am building a model that predicts the SOH of a lithium ion battery. My data are from 600 battery charge cycles as follows: for each cycle I have 3 curves each of length 128 samples: voltage, current ...
deckard1992's user avatar
1 vote
1 answer
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Would AlphaZero perform better if made with transformers?

AlphaZero utilized a residual convolutional neural network to estimate move policy and position value. If it was rebuilt today, would it be more efficient and powerful if they used a transformer ...
Ben G's user avatar
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What are the differences between Inception Score and Fréchet Inception Distance?

From the articles I've read about image generation using GANs, the Inception Score measures two things simultaneously: the variety of images (diversity) and the distinct quality of each image. Does ...
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adversarial training on convnext shows a very strange curve

i am currently working on a research project where I have to train some models for adversarial robustness. I have implemented the algorithm used by a research paper called adversarial training for ...
M Akrm's user avatar
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Classifying Images that Look Like Noise

I'm about to build a system that is supposed to evaluate images (900 x 150) like the following and classify it in to one of five categories: image that looks like noise In case you're wondering, they'...
Ed Park's user avatar
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How can the discriminator determine the sample is fake or real?

Based on the articles I've read, the discriminator can identify whether a sample is fake or real. However, the articles don't clarify the conditions used to determine if a sample is fake or real. I ...
David's user avatar
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What are meaning of parameters $\theta$ in this context?

I'm reading the article about generative model from Open AI, here is the section where they explain them: Our network is a function with parameters $\theta$, and tweaking these parameters will tweak ...
David's user avatar
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What's the best criterion for evaluating activation maps in a CNN?

I'm currently studying CNNs and I had the idea of building a model without a fully connected layer at the end. I think this could be beneficial, if one can somehow model the desired outputs as a ...
Pietro's user avatar
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What is the matrix representation of a dilated convolution?

I am delving into the matrix representation of dilated convolutions, especially after understanding standard 1D convolutions as Toeplitz matrix-vector multiplications. My specific focus is on Dilated ...
Yanirmr's user avatar
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Does ResNext split data or copy it?

I have been learning how to create ResNext neural networks, and am confused how input works with cardinality. In this answer, it seems that it's saying that the data is added together, which I assumed ...
eop3's user avatar
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Help on calculating the right feature vector size from a CNN

I need some help calculating the feature vector size for this network I'm working on from a paper. It says the input image is 96x96x1. The first convolution layer is a filter of 5x5 with a stride of 2,...
Sanjeev Nahulanthran's user avatar
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How does the Yolo loss match bounding boxes to the ground truth?

I've been going over the YOLO paper again and I was wondering something about the loss. Yolo divides an image into grids and then for each grid can have multiple bounding boxes to detect multiple ...
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Why CNN filters (kernels) are randomly initialized?

I learned that when CNN filters are defined, they are initialized with random weights and bias(Im not sure about bias). Then as learning step goes on, the weight values change and each filter makes ...
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Is number of output filters right way to generate multiple output feature maps?

I am doing a CNN U-Net localization network that works on a way of scalar regression of 2D plane using Gaussian peaks in Keras. I have dataset that contains 10 different keypoint PNG's (each about 50k ...
wortelus's user avatar
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How to detect abnormal fetal head size with image classification?

I am a computer science student currently working on my final project, which involves finding a classification-based solution for predicting the head size of a fetus during its third month. Here is a ...
NitaStack's user avatar
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Cannot find appropriate model to classify hidden states

My input data is vector representing encoded image - 22 features, and I try to classify by 3 classes 0, 1, 2 (neutral, good, bad) Original: ...
Max Usanin's user avatar
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Is there a better way for my CNN to handle random values?

I made an autoencoder to, ideally, turn an image into seemingly random numbers(Using a loss that determines randomness) and turn those random numbers into the original image. The results were kind of ...
Nathanael Suarez's user avatar
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Support Vector Machine used in Image Classification

I am looking for a reference preferably a paper that details how Support Vector Machines (SVMs), and OpenCV were used to perform image classification before Convultional Neural Networks (CNNs). I am ...
Jose M Serra's user avatar
3 votes
1 answer
84 views

Understanding the function of attention layers in a convolutional neural network (U-Net in a diffusion model)

I am trying to understand the neural network architecture used by Ho et al. in "Denoising Diffusion Probabilistic Models" (paper, source code). They include self-attention layers in the ...
Rational Function's user avatar
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Design approach for image classification regarding genders

I am new to the world of AI and wanted to ask your guidance on how to design a ML model to classify genders based on images. There will be only one person in the image. The person could be kids, ...
Doug's user avatar
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Ways to train a neural network continuosly as new data is added [duplicate]

There is a project I'm currently working on that requires object detection with continuous training. The idea is to train a model beforehand with a standard dataset. When I get new images I want to &...
Pedro Carvalho's user avatar
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ViT fails to detect a white pixel in a black image

I wanted to report you to some experiments in the context of Deep Learning for Computer Vision, in particular for visual reasoning. The main question I am trying to answer is the difference between ...
Fiorenzo Parascandolo's user avatar
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0 answers
92 views

Is improving a Neural Network really just "trial and error"?

After asking on StackOverflow, I was redirected here, so I'm reposting this question. I am a PhD student in Computational Physics and I've started to study a bit of Neural Networks, and decided to try ...
Mauro Giliberti's user avatar
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2 answers
87 views

Why does test data need to be labelled? [closed]

I have a problem understanding why test data needs to be labelled to test a trained faster R-CNN model. Maybe it's basic, but I don't get why it needs to be labelled. When an image is not obvious, ...
Alexandru Lebada's user avatar
1 vote
1 answer
497 views

Which epoch is the best for me to choose?

I have trained my deep learning model. I also saved the validation loss to a file and plotted on a graph I have $2$ questions for this: Does the validation loss look normal? Is there any issue with ...
user avatar
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1 answer
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Is size of trained model on disk a good measure of model complexity?

I am writing a research paper on my own custom CNN model for image classification. I am comparing my model architecture with pre-trained architectures, like DenseNet121 and InceptionV3. I want to ...
Dawood Ahmad's user avatar
1 vote
1 answer
79 views

Do GANs have constant running time?

After the model is trained, you just need to input random noise and the generator will output an image, does this mean GANs have constant running time ? I'm asking about both naïve GAN and variants of ...
user avatar
1 vote
1 answer
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Why do we do need compression in Semantic Segmentation?

When doing semantic segmentation, we often make use of FCN, which can be thought of in two parts: an encoder and decoder. As I understand, the encoder compresses the image into a spatially small, but ...
Dude156's user avatar
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2 votes
2 answers
207 views

The training process of a conditional GAN

For example, consider a dataset like MNIST. I give the conditional vector to produce only the number $7$ for both the generator and discriminator. In the following scenarios, what will the ...
abcd's user avatar
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0 answers
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When to know if I am "on the right track" for a CNN architecture

Context Very new to CNNs and ML in general. I am building a simple binary image segmentation network for generating black and white image masks (white pixels = desired object; black pixels = all else)....
gladshire's user avatar
0 votes
1 answer
54 views

Is it possible to build a convolutional autoencoder with fully connected bottleneck with low dimension?

I want to do a project with a small size image dataset (the size is about 50*50). There's another similar dataset, and I want to prove that the datasets are different. I built a convolutional ...
Kekai's user avatar
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1 vote
1 answer
220 views

In the conditional GAN (cGAN) architecture, why does the discriminator need conditional variable?

I'm reading about conditional GAN (cGAN) architecture, what I know is that the generator creates images combining both noise vector and conditional variable, the noise vector brings in random elements ...
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