Questions tagged [convolutional-neural-networks]

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

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40 views

How can one be sure that a particular neural network architecture would work?

Traditionally, when working with tabular data, one can be sure(or at least know) that a model works because the included features could explain a target variable, say "Price of a ticket" ...
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60 views

How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling?

I am trying to implement a paper on Image tempering detection and localization, the paper is Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks, I was ...
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38 views

Why does the number of channels in the PointNet increase as we go deeper?

For example, in PointNet, you see the 1D convolutions with the following channels 64 -> 128 -> 1024. Why not e.g. ...
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37 views

What is the amount of test data needed to evaluate a CNN?

I have an image dataset of about 400 images. 70% of these data points were used for training, 15% for validation, and 15% for testing. I am using the 70% to train a CNN-based binary classifier. I ...
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40 views

Can text-independent writer identification be done without multi-sentence training datasets for each writer?

I am trying to learn more about text-independent writer identification and was hoping for some advice. I have a folder with 100k images, each of them with a different handwritten sentence. All of the ...
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1answer
47 views

How to quickly change hand-drawn shapes to symmetrical polished shapes?

Given a hand-drawn shape, I'd like to generate the corresponding symmetrical polished shapes such as circle, rectangle, triangle, trapezoid, square, parallelogram, etc. A short video demonstration ...
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31 views

Suppress heatmap non-maxima in segmentation with UNet

I'm using U-Net for image segmentation. The model was trained with images that could contain up to 4 different classes. The train classes are never overlapping. The output of the UNet is a heatmap (...
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31 views

How to measure/estimate the energy consumption of CNN models during testing?

Does someone know a method to estimate / measure the total energy consumption during the test phase of the well-known CNN models? So with a tool or a power meter... MIT has already a tool to estimate ...
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1answer
59 views

Can fully connected layers be used for feature detection?

I need help in understanding something basic. In this video, Andrew Ng says, essentially, that convolutional layers are better than fully connected (FC) layers because they use fewer parameters. But I'...
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60 views

When to use convolutional layers as opposed to fully connected layers?

I am still new to CNNs, but I would like to check my understanding between when to use convolutional layers versus fully connected layers. From what I have read, we can use convolutional layers with ...
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69 views

Atari Games: Pretrained CNN to accelerate training?

DQN for Atari takes considerable training time. For example, the 2015 paper in Nature notes that algorithms are trained for 50 million frames or equivalently around 38 days of game experience in total....
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31 views

How much data do we need for making a successful de-noising auto-encoder?

Is there a guide how much data do you need for making successful denoising model using autoencoders? Or the rule is, the more data, the better it is? I tried with small dataset 350 samples, to see ...
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22 views

How is the performance of a CNN trained with monochrome images on image recognition tasks degraded?

For CNN image recognition tasks, like object recognition/face recognition/object segmentation/posture recognition, are there experiment results about how much will the performance be degraded with ...
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21 views

Are there references for real-time 3D shape measurement using convolutional neural network?

I want to develop a real-time 3D shape measurement using convolutional neural network (CNN) for vehicle. Could you recommend me references for that? Thanks.
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31 views

Overcome caveats on using Deep Learning for faster inference on limited performance availability

I am working in the field of Machine Vision, where accuracy and performance both play a major factor in deciding the approach towards a problem. Traditional rule based approaches work quite well in ...
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37 views

What is the reason for different learned features in upper and lower half in AlexNet?

I was reading AlexNet paper and the authors quoted the kernels on one GPU were "largely color agnostic," whereas the kernels on the other GPU were largely "color-specific." The upper GPU takes ...
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29 views

How to calibrate model's prediction given past images?

I want to predict how open is the mouth given a face image. It's a regression problem (0= mouth not open, 1=mouth completely open). And something between 0 and 1 is also allowed. ConvNet works fine ...
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141 views

Can we force the initial state of a neural network to produce an “unknown” class?

Has anyone investigated ways to initialize a network so that everything is considered "unknown" at the start? When you consider the ways humans learn, if something doesn't fit a class well enough, it ...
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28 views

Deep learning techniques with time-fixed, time-dependent and imaging data

I have a question about the use of deep learning techniques with time-fixed features and images (setting 1) and time-dependent features (setting 2). (I am pretty new to the deep learning world so ...
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33 views

Banding artifacts in CNN

I was working on a CNN for HDR image generation from LDR images. I used an encoder-decoder architecture and merged the input with the decoder output. However I'm getting some banding artifacts in the ...
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26 views

How can I find the similar non-zero connections between different levels of sparsity of the same network?

I am pruning a neural network (CNN and Dense) and for different sparsity levels, I have different sub-networks. Say for sparsity levels of 20%, 40%, 60% and 80%, I have 4 different sub-networks. Now, ...
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23 views

Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
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36 views

How do we choose the filters for the convolutional layer of a convolution neural network?

Since the hidden layers of a CNN work as a trainable feature extractor, more detailed content based on a larger number of pixels shall require bigger filter sizes. But for cases where localized ...
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19 views

How do CNNs or RNNs “stack the feature of nodes by a specific order”?

I am trying to understand the following statement taken from the paper Graph Neural Networks: A Review of Methods and Applications (2019). Standard neural networks like CNNs and RNNs cannot handle ...
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19 views

Super-Resolution with Convolutional Neuronal Networks, why interpolation at the beginning?

I have read several papers about super-resolution with CNNs, where a low-resolution image is reconstructed to a high-resolution image. What I don't understand is, why it is necessary to interpolate ...
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32 views

What do the authors of this paper mean by the bias term in this picture of a neural network implementation?

I am reading a paper implementing a deep deterministic policy gradient algorithm for portfolio management. My question is about a specific neural network implementation they depict in this picture (...
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27 views

An Encoder-Decoder based CNN to predict a tensor of points

So I have with me a data of rendered 2D images of a 3D object and along with that, I have the image projection coordinates (X, Y) of all the voxels that are in the ...
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2answers
85 views

Backpropagation of neural nets with shared weight

I am trying to understand the mathematics behind the forward and backward propagation of neural nets. To make myself more comfortable, I am testing myself with an arbitrarily chosen neural network. ...
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0answers
37 views

Automating browser actions using AI

I am at a very initial stage of my research so I will try to describe what I am trying to achieve: I want to create an AI model which learns how to navigate the browser's component like clicking or ...
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24 views

How come a detection works after global average pooling 2D?

I use an off-the-shelf convolutional neural network, where at the end of the convolutional part, the depth of the last convolutional layer is expanded and then its 2D average is computed (such that ...
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15 views

How to estimate the convolutional representation of a graph from its similarity to other graph convolutional representation?

Suppose we have two graphs A and B disconnected to each other (let's say 2-hops each), within a larger graph. If the convolutional representation of graph A is known, is it possible to estimate the ...
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45 views

What is the difference between FC and MLP in as used in PointNet?

I am trying to understand the PointNet network for dealing with point clouds and struggling with understanding the difference between FC and MLP: "FC is fully connected layer operating on each ...
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20 views

How would semantic segmentation work with a non convolutional neural network

Listening to lectures, convolutional neural network seems to be an improvement over a simple neural network, where for example, you take every pixel in the image, flatten it to a vector, and feed it ...
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2answers
35 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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22 views

Is using a filter of size (1, x, y) on a 3D convolutional layer the same as using a filter of size (x,y) on a 2D convolutional layer?

I'm trying to predict some properties of videos with Keras using the following rough architecture: Feed each frame through the same 2-D convolutional layer. Take the outputs of this 2-D ...
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66 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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191 views

What are the actual math or computer science concepts behind these unfamiliar hyperparameters in the Deep Dream Generator's Deep Style?

I've been playing around with neural style transfer for a about a year now, and I've been doing it with two general approaches. The first has been using a script that is available on the Keras GitHub, ...
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61 views

What is the gradient of a non-linear SVM with respect to the input?

The objective function of an SVM is the following: $$J(\mathbf{w}, b)=C \sum_{i=1}^{m} \max \left(0,1-y^{(i)}\left(\mathbf{w}^{t} \cdot \mathbf{x}^{(i)}+b\right)\right)+\frac{1}{2} \mathbf{w}^{t} \...
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34 views

NN for card game evaluation function

I've written an Monte Carlo Tree Search player for the game of Castle (AKA Shithead, Shed, Palace...). I have set this MCTS player to play against a basic rule based AI for ~30000 games and collected ~...
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69 views

What is the difference between using dense layers as opposed to convolutional layers in my networks when dealing with images?

I am thinking about developing a GAN. What is the difference between using dense layers as opposed to convolutional layers in my networks when dealing with images?
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35 views

Why do DeconvNet use ReLU in the backward pass?

Why does DeconvNet (Zeiler, 2014) use ReLU in the backward pass (after unpooling)? Are not the feature maps values already positive due to the ReLU in the forward pass? So, why do the authors apply ...
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28 views

What are the main points of the top-down vs bottom-up paradigm in neural networks?

I've been reading some papers on human pose estimation and I'm starting to see the terms top-down and bottom-up crop up a lot. For example in this paper: Our hourglass module differs from these ...
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34 views

Human Aggression Detection Community, Competition and dataset

I'm looking for a community or competition website related to human aggression detection using Deep Learning in a video. Also, I'm looking for a dataset of human aggression activities. Any ...
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26 views

Can denoising auto-encoders be convolutional and fully connected?

I have been reading lately on autoencoders a lot. I just wanted to summarize my understanding of denoising autoencoders. As far as I understand they can be Fully connected (in which case, they will ...
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0answers
62 views

Dealing with very similar object classes in object detection

I'm working on an object detection problem using Faster R-CNN. I need to identify two object classes, and they are very similar to one another. Furthermore they are similar to a third type of object ...
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27 views

Face recognition model loss not decreasing

I wrote a script to do train a Siamese Network style model for face recognition on LFW dataset but the training loss doesnt decrease at all. Probably there's a bug in my implementation. Could you ...
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16 views

Convolutional Feature Encoding Methods in DCNN

In Computer Vision, feature encoding methods are used on pre-trained DCNN to increase the feature robustness to certain conditions such as viewpoint/appearance variations ref. I was just wondering if ...
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33 views

How to adapt MTCNN to large images with relatively small ROIs

This question could be generalised to how to adapt state-of-the-art object detection models to large images with small ROIs. In my particular case I'm trying to use this implementation of MTCNN to ...
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129 views

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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34 views

Training an unsupervised convolutional neural network to learn a general representation of a Lua module

I am trying to train a CNN in keras to learn a general representation of a Lua module, e.g. requires at the beginning, local variables, local functions, interface (returns) and in between some ...

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