Questions tagged [convolutional-neural-networks]

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

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Deep NN architecture for predicting a matrix from a matrix and list of floats

I am trying to predict a matrix (size RxC) based on an input matrix (size RxC) and a list of floats ...
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Is reconciling shape discrepancies the only purpose of padding?

Padding is a technique used in some of the domains of artificial intelligence. Data is generally available in different shapes. But in order to pass the data as input to a model in deep learning, the ...
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Feeding the output back to input in 3D CNN model

I am currently designing a Model which takes Input 3D Grid and Model Output at $t-1$. The model figure is described below I have two thoughts in training the model for above situation. Feed output $...
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How Tesla and other companies use outputs from neural networks to drive the car?

Here is the short description of Tesla Autopilot AI: https://www.tesla.com/autopilotAI And here are some videos about how Tesla uses neural networks: Andrej Karpathy - AI for Full-Self Driving at ...
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42 views

Is it possible to use deep learning to generate a 2D image from a few numerical values?

Is it possible to train a DL model that will generate a full resolution 2D image based on few numbers describing this image and what type of model or architecture would that be? What I want to ...
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Scrabble-MuZero: combine observation planes of different shape

I'm working on an implementation of Scrabble with MuZero. The board state is represented by a matrix with shape $15 \times15 \times 27$ ($26$ letters $+ 1$ wildcard, value $0/1$) and the rack state $...
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Is there any animation that illustrates the “fold” and “unfold” operations of convolutional layers?

There are fourteen convolution layers in PyTorch. Among them six are related to convolution, another six are related to transposed convolution. The remaining two are fold and unfold operations. The ...
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Is there any gain by lazy initialization of weights, biases and number of input channels for a convolution operation?

The basic layers for performing convolution operation in PyTorch are ...
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Prune Neural Networks layers for f% sparsity - TensorFlow2

I am using TensorFlow 2.5 and Python3.8 where I have a simple TF2 CNN having one conv layer and an output layer for binary classification as follows: ...
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The ratio between number of units in multi-input model

I have the model that accepts two inputs: Image from camera Speed of the car I can create some CNN layers to process the image input and some MLP layers to process other type of data (for example ...
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How can I compute a mathematical formula for my CNN?

Let's say, for example, I have built the following CNN model using Keras: ...
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How is the convolution operation connected to neural networks?

I've been reading up on the convolution operation and neural networks. I understand that the convolution operation is defined as: $$(f * g)(t)=\int_{-\infty}^{\infty} f(\tau) g(t-\tau) d \tau$$ The ...
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What is the primary paper demonstrating that CNNs struggle with datasets containing ambiguities? [closed]

It is known that neural networks, such as convolutional neural networks, struggle with pattern recognition if training sets contain ambiguities (i.e. several labels can correspond to one and the same ...
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Using a rectified Tanh to train a CNN?

I have been experimenting with activation functions on CNN, and it occurred to me to use a rectified tanh function. So that is basically if z > 0 tanh(z) else 0. ...
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Comparing results of different image splicing methods on a part of the CASIA 2.0 dataset

So I am working on an image splicing detection algorithm using ResNet-50 model. I am using the CASIA 2.0 dataset which consists of 7491 Authentic images and 5123 Fake images. However out of the fake ...
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How to get output as “saliency map”/“heatmap” of a tenserflow CNN model in a webapp deployed in HEROKU using flask? [closed]

I can implement saliency map of image in notebook . But , how can i get the saliency map / heatmap as output along with predition of tuberculosis . I deployed the webapp in HEROKU using flask.
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Does randomly adding hand-engineered features increase the CNN's sample efficiency/performance?

It is a known fact that preprocessing images using CV techniques will improve CNN performance (see this answer). But what happens when you feed in the entire image and the filtered image randomly to ...
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What does “differentiable architecture” mean?

I'm currently reading a paper that uses CNN's as a base approach to solving some image classification issues and I've found that they kept mentioning the term "Differentiable Architecture", ...
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What image augmentations can help a neural network identify the smallest pixels within an image?

I am training a CNN to identify objects and I believe the network will learn much faster if it can learn to focus on the smallest pixels. One way to go about this would be to augment the images before ...
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What causes high differences in neural network accuracy each run?

I trained a CNN using Keras in R to multi-dimensional image data for image classification of five classes. I realized that each run (I retrained the network on the same data for ten times), although I ...
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Computational complexity of a CNN network

In the following network, the convolution operations of convolutional blocks are performed by three 1-D kernels with the sizes 8, 5, and 3 respectively along with stride equal to 1. The final network ...
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Are there any advantages of the local attention against convolutions?

Transformer architectures, based on the self-attention mechanism, have achieved outstanding performance in a variety of applications. The main advantage of this approach is that the given token can ...
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Can some of the weights be fixed during the training of a neural network?

Is it possible to exclude specific layers from the optimization? For example, let's say I have an input layer, 2 hidden layers, and the output layer. I know there is a perfect solution for my problem ...
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How to scale Computer Vision? How to implement Emotion detection from live video feed of N different video simultaneously?

I have a pipeline based on Scaled Yolov4 detection algorithm for faces which extract faces of users and then uses a CNN to ...
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Image classification distributed inference (mobile/server)

I'd like to learn some stuff about distributed DNN inference and how it works in practice. So, let's consider the example of image classification and assume we have a mobile device which utilizes the ...
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Convolutional Neural Network (CNN) with Tree architecture to organize the number of classes

At the moment, I have around 1.000 classes with accuracy and loss that are acceptable. In the long term, there could be more than 100.000 classes. The main problem is that every time a new class is ...
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What is a Silhouette Neural Network

I was going through a study in which I found something called a dilated Silhouette Neural Network. I want to know what it is, what it can do, and how it is better from a CNN? Link to the journal: Link
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Vector input to CNN for object detection

I am training a 3D object detection network (Retinanet-based as of the moment) for re-detecting tracked objects. I would like to be able to add the velocity vector of the tracked object as an input to ...
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Is “Pruning” only applicable to CNNs?

What Is Neural Network Pruning And Why Is It Important Today? The above article only talks about Convolutional Neural Networks: One of the first methods of pruning is pruning entire convolutional ...
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Looking for advice on how to train an AI object detection algorithm to recognize smaller objects than what it has been trained on

For some academic work, I am training an AI object detection algorithm (TensorFlow models) to look for specific objects (plants, in my case). I am taking photos with a hand-held camera, and am having ...
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Regress values inside the bounding boxes to predict a value in Object Detection

I am currently working on an object detection task. I have a dataset of Grayscale and Depth Images. The annotation format is x1, y1, x2, y2, class, depth. I have calculated this depth (of each object/...
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More channels vs multiple inputs in neural network

Suppose I want to train the model for playing chess. I found that existing models use as input the grid with dimensions 8x8x20 (so we have 20 channels). Some channels may represent how different kind ...
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Loss function to Push response value towards extremes

I have a feature map whose values are in the range of [0,1]. I want to push these values either towards extreme 0 or 1 using some loss function. Since I don't have any target value so it had to be in ...
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AI model to predict/generate person's image

I want to make a model that predicts person's shape depending on his son's image. My plan is to create a dataset and each data point in it consists of two images; One for the father or mother and one ...
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What gets optimized in convolutional neural network?

In a convolutional neural network, the hyperparameters such as number of kernels and stride, kernel size, etc are determined. After some combination of convolutions, ReLU and pooling layer there is ...
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How to change number of trained layers in object detection TensorFlow models?

Training custom object detection models with TensorFlow usually means a transfer learning of pre-trained models and, if I understand it correctly, it means only training the few last layers, with ...
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How can a CNN be used in machine translation?

How can a convolutional neural net (CNN) be used in machine translation? Convolution is a mathematical operation, so how are natural languages translated into matrices? e.g., DeepL_Translator#...
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In CNN, how the conversion of convolution layer to fully connected layer decides the no. of kernel

I am trying to understand the shape of the activation map after every operation. Here is the model summary . All is clear, but from the point labeled 1, how 7x7x512 turns out to be 4096 specifically ...
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How is the size of the class activation maps related to the size of the input images?

According to the images of the block diagram of class activation maps on the Internet, it seems that these images are a weighted sum of feature maps of the last convolutional layer as shown in the ...
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How is parameter sharing done in CNN?

I am trying to understand the concept of parameter sharing in a convolution neural network from Parameter Sharing. I have a few confusions: Parameter sharing refers to the fact that for generating a ...
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What is the conceptual difference between convolutional neural networks and auto-encoders?

I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter: I wonder how it is different from Auto-Encoders: For me, it ...
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Dilated CNN - How to deal with odd lookbacks?

I'm currently working with a dilated CNN to solve a regression problem. I am trying to forecast 24 timesteps ahead based on 6, 12 and 24 values (lookback). However I am not sure which dilation rate ...
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Evaluating a convolutional neural network on an imbalanced (academic) dataset

I have trained a posture analysis network to classify in a video of humans recorded in public places if there is a) shake-hand between two humans, b) Standing close together that their hands touch ...
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How to change a single object detection network to a multiple object detection network?

I have trained a CNN network to detect a circle and approximate its centre and radius in an image. What I want to do now is detect the centre and radius of all the circles if there are multiple ...
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How can I get the images with the highest activation for a given unit?

I am new to machine learning. I am working on the pretrained AlexNet on Pytorch and i would like to visualize the receptive fields of a given unit U. To do that I am trying to give like 200K images as ...
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Are these visualisations the filters of the convolution layer or the convolved images with the filters?

There are several images related to convolutional networks on the Internet, an example of which I have given below My question is: are these images the weights/filters of the convolution layer (the ...
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Feeding CNN FFT of an image, a dumb idea?

My dataset consists of about 40,000 200x200px grayscale images of centered blobs bathed in noise and occasional artifacts like stripes other blobs of different shapes and sizes, fuzzy speckles and so ...
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Why and when transformers are better than CNN's in sequence modeling tasks?

Transformers have made a revolution in the domain of NLP and gave rise to a rapid boost of neural networks in a variety of language modelling problems, TTS and, recently, achieved competitive accuracy ...
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What is the best way to train neural network with imbalanced mixed data (images and structured data)?

I have structured data and image data to solve a regression problem. One sample of structured data can be related to N images. If I use only structured data, I get decent performance, but not enough ...
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CFD Reinforcement Learning Topology optimization wind tunnel

I want to create a reinforcement learning environment, designed for win tunnel simulations, where for each iteration a deep convolutional model could receive the 3D vector/scalar fields from the past ...

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