Questions tagged [convolutional-neural-networks]

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

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Are there any works that deal with 2D pose estimation in videos?

Since pose estimation is often a task where spatial-temporal context should be helpful in finding subsequent key points, I thought there should be many papers on it. However, I could not find any work ...
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63 views

Showing first layer RGB weights similarly to AlexNet

I would like to show the RGB features learned in the first layer of a convolutional neural network similarly to this visualization of the same layer's features from AlexNet: My learned weights are in ...
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Problem extracting features from convolutional layer where the dimensions are big for feature maps

I have trained a convolutional neural network on images to detect emotions. Now I need to use the same network to extract features from the images and use them to train an LSTM. The problem is: the ...
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168 views

Would YOLO be able to detect objects in "different" positions?

I have the following question about You Only Look Once (YOLO) algorithm, for object detection. I have to develop a neural network to recognize web components in web applications - for example, login ...
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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 as intermediate ...
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74 views

How can I sample the output distribution multiple times when pruning the filters with reinforcement learning?

I was reading the paper Learning to Prune Filters in Convolutional Neural Networks, which is about pruning the CNN filters using reinforcement learning (policy gradient). The paper says that the input ...
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104 views

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 operations 1,2,3 in PyTorch are ...
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2answers
55 views

Heavily mixing signal differentiation from Open Set of backgrounds via CNN

To whomever can help out, I appreciate it. I am currently attempting to detect a signal from background noise. The signal is pretty well known but the background has a lotttt of variability. I've ...
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1answer
119 views

Is down-sampling the only purpose of using stride?

Stride is used in at least two operations: convolution and pooling. Both operations can be viewed as applying a kernel function on input using a kernel (filter). Stride determines the amount of "...
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52 views

What is the stride information of an image referring here?

In convolutional neural networks, the convolution and pooling operations have a parameter known as stride, which decides the amount of jump the kernel needs to do on the input image. You can get more ...
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28 views

Is image machine translation done in two steps?

Suppose I have images of hand-written Japanese text. If I want to translate those images, would my ML algorithm be a 2-step model (for example, a CNN to convert the image into Japanese characters/...
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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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808 views

Is my understanding of how the convolution with stride 2 works in this example correct?

I'm currently reading this explanation of convolutional neural networks and there's a part around strides that I don't quite understand. I'm just starting with this, so I apologize if this is a really ...
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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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What is the intuition behind the number of filters/channels for each convolutional layer?

After having chosen the number of layers for a convolutional neural network, we must also choose the number of filters/channels for each convolutional layer. The intuition behind the filter's spatial ...
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what are the factors that make different Kernels in CNN? [duplicate]

what are the factors that affect the number of kernels and filters on CNN ? like one is 256 sometimes or 128 .. What does this change depend on? what is the difference between kernel and filter in the ...
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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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How to colorize images with Variational Autoencoder?

CONTEXT I'm trying to colorize images with Variational Autoencoder. Input is 256x256 gray image. Output is 256x256x2 as I convert image to a LAB color space and then put gray channel as input and ...
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How can I detect thin objects (like pens and pencils) without a bounding box but only 2 endpoints and the orientation?

I am looking to detect thin objects, like pens, pencils, and surgical instruments. The bounding box is not important, but I am looking to see if I can train a model to detect both the object as well ...
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1answer
50 views

Attention mechanism: Why apply multiple different transformations to obtain query, key, value

I have two questions about the structure of attention modules: Since I work with imagery I will be talking about using convolutions on feature maps in order to obtain attention maps. If we have a set ...
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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
59 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
22 views

Can we change bias and control the output of neural network?

I have read the use of Targeted Adversarial Attacks for making the model perform better. But can we change the bias of the neural networks and control the outcome of the network rather than changing ...
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1answer
58 views

Can CNNs detect image similarity?

I have been running some experiments to see whether a CNN can detect whether two images are the same. However, I can't seem to make it work. I am wondering whether CNNs are not able to do what I am ...
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58 views

How to calculate the precision and recall given the predictions and targets in this case?

I'm using three pre-trained deep learning models to detect vehicles and count from an image data set. The vehicles belong to one of these classes ['car', 'truck', 'motorcycle', 'bus']. So, for a ...
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What is meant by "lateral connection" in the context of neural networks?

A class of CNN is popular due to the implementation of residual connections. We can use both terms "residual connections" and "skip connections" interchangeably as they refer to ...
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How to force/instruct CNN to learn specific features?

Let's say I have a CNN that classifies shirts. And let's say that it performs poorly on shirts that have horizontal stripes. How would I force network to put more emphasis on shirts with horizontal ...
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31 views

Is the phrase "Feature Pyramid Network" refer to CNN only?

"Feature Pyramid Network" is a network that is used for feature extraction. Since it is pyramid in shape, it might be called so. Consider the following excerpts from two different sources #1 ...
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260 views

Are these book example CNN results realistic?

I've been following a deep learning book and the current section I'm on is about convolutional neural networks. The author presents some code to create a basic CNN with about 1 million parameters, ...
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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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Neural network for recognizing ship types based on location series

I am building a neural network for recognizing ship types based on a 1000-long series of location data (latitude-longitude, normalized to account for different km/longitude° metrics, so that vector ...
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1answer
47 views

How many layers and neurons in a FFNN do I need to make it equivalent to a CNN?

I started to learn machine learning early, and I studied the convolutional neural network and its ability to understand images and how it helps to reduce the number of parameters that need to be tuned....
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93 views

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

So, I'm using a pretrained PNASNet-5-Large model to do some image classification. In the file, it says that the input range is in [0,1] (I'm assuming pixel values of input images). The images I have ...
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1answer
95 views

How do GPUs faciliate the training of a Deep Learning Architecture?

I would love to know in detail, how exactly GPUs help, in technical terms, in training the deep learning models. To my understanding, GPUs help in performing independent tasks simultaneously to ...
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320 views

What loss function should one use for object detection, knowing that the input image contains exactly one target object?

What loss function should one use, knowing that the input image contains exactly one target object? I am currently using MSE to predict the center of ROI coordinates and its width and height. All ...
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Augmented an Image with other data when training CNN

In the typical RL/MDP framework, I have offline data of $(s,a,r,s')$ of expert Atari gameplay. I'm looking to train a CNN to predict $r$ based on $(s, a)$. The states are represented by a $4 \times 84 ...
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88 views

How should continuous action/gesture recognition be performed differently than isolated action recognition

I am going to train a deep learning model to classify hand gestures in video. Since the person will be taking up nearly the entire width/height of the video and I will be classifying what hand gesture ...
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825 views

Is max-pooling really bad?

Hinton doesn't believe in the pooling operation (video). I also heard that many max-pooling layers have been replaced by convolutional layers in recent years, is that true?
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How can we compute the gradient of max pooling with overlapping regions?

While studying backpropagation in CNNs, I can't understand how can we compute the gradient of max pooling with overlapping regions. That's also a question from this quiz and can be also found on this ...
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1answer
103 views

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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96 views

How is the convolution layer is usually implemented in practice?

Following an earlier question, I'm interested in understanding the basics of Conv2d and especially how the kernel is applied, summed, and the propagated. I ...
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228 views

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling?

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling? If not, why do they perform as well as networks which use max-pooling?
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260 views

In which scenario would you want to have two adjacent pooling layers?

In which scenario, when assembling a CNN, would you want to have two adjacent pooling layers, without a convolutional layer in between?
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1answer
1k views

Is a non-linear activation function needed if we perform max-pooling after the convolution layer?

Is there any need to use a non-linear activation function (ReLU, LeakyReLU, Sigmoid, etc.) if the result of the convolution layer is passed through the sliding window max function, like max-pooling, ...
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427 views

What is the effect of using pooling layers in CNNs?

I know how pooling works, and what effect it has on the input dimensions - but I'm not sure why it's done in the first place. It'd be great if someone could provide some intuition behind it - while ...
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1k views

What are the benefits of using max-pooling in convolutional neural networks?

I am reading Francois Chollet's Deep learning with Python, and I came across a section about max-pooling that's really giving me trouble. I am unable to copy-paste the content, so I've included ...
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What's the difference in using multiple convolutional layers and no pooling versus using a single convolutional layer and a single max pooling layer?

I'm currently working on a college project in which I'm designing a Deep Q-Network that takes images/frames as an input. I've been searching online to see how other people have designed their ...
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695 views

How many weights does the max-pooling layer have?

How many weights does the max-pooling layer have? For example, if there are 10 inputs, a pooling filter of size 2, stride 2, how many weights, including bias, does a max-pooling layer have?
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417 views

Is it effective to concatenate the results of mean-pooling and max-pooling?

Is it popular or effective to concatenate the results of mean-pooling and max-pooling, to get the invariance of the latter and the expressivity of the former?
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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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