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Questions tagged [convolutional-neural-networks]

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

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2
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1answer
39 views

When and how to use a mix of loss functions for back-propagation?

I am trying to understand the best loss function to be used with a convolutional neural network. I came to know that we can mix two loss functions. Can any body share in what case was it done and how?
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2answers
72 views

What are the standard problems for CNNs and LSTMs?

What are the standard (or baseline) problems (or at least common ones) for CNNs and LSTMs? As an example, for a feed-forward neural net, a common problem is the XOR problem. Is there a standard ...
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2answers
45 views

Do bounding boxes increase accuracy in and of themselves?

Say I have a standard image classification problem (ie: CNN is shown a single image and predicts a single classification for it). If I were to use bounding boxes to surround the target image (ie: ...
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1answer
154 views

What should a good loss curve look like?

This is a very basic question. I'm running a faster rcnn trainer on a dataset for object recognition. My images range from 200x200 to 7360x4912 in resolution. There are only 2 classes being trained (...
0
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1answer
77 views

Artifacts After pruning Unet CNN

Im trying to make a dark image brighter using CNN-UNet arcitecture. When I train the network I get the following results: When I cut the features in half for pruning, and do full train again, I get ...
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1answer
64 views

Should I use single or double view for gender recognition?

My project requires gender recognition of people shown on the given images, with more than one person per image. However, these people can be positioned in frontal or side view(passing by ...
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1answer
58 views

Neural Network to estimate distance

I built a three layer neural network (first is 1D Convolutional and the remaining two are Linear). It takes an input of 5 angles in radians, and outputs two numbers from 0 to 1, which are respectively ...
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1answer
18 views

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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2answers
76 views

Which neuron represents which part of the input?

In a neural network, each neuron represents some part of the input. For example, in the case of a MNIST digit, consider the stem of the number 9. Each neuron in the NN represents some part of this ...
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0answers
15 views

Computing layers dimensions for deep learning architectures

I have already a few projects in deep learning under my belt. However, there is one fundamental thing that has come to my mind recently while trying to implement my own architecture. Looking at the ...
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1answer
16 views

What should load_mask() return if an image doesn't have any objects? (Mask RCNN)

I want to use Mask RCNN to do image segmentation. I need to override the load_mask function for the dataset class. I know this function should return mask tensors and class ids of objects in an image. ...
2
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1answer
25 views

Transfer learning to train only for a new class while not affecting the predictions of the other class

I am basically interested in vehicle on the road. YoloV3 pytorch is giving a decent result. So my interested Vehicles Car ...
2
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0answers
37 views

CNN clasification model loss stuck at same value

I have CNN model to classify 2 classes. (Yes or No) I use categorical_crossentropy loss and softmax activation at the end. For input I use image with all 3 channels, for output I use One hot encoded ...
2
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1answer
683 views

What are the counterparts of non-linearities and dropout in fully convolutional networks?

I am trying to replicate the fully convolutional networks (FCN) concept described here for semantic segmentation. It seems people have successfully trained such models by removing fully connected ...
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0answers
58 views

What to do when an image classifier does good for a class but bad for another?

So I wrote a convolutional neural network for a binary image classification. I have around 5300 images for each class which I thought would be enough to at least give me a good accuracy on the ...
4
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1answer
82 views

What is the difference between graph convolution in the spatial vs spectral domain?

I've been reading different papers regarding graph convolution and it seems that they come into two flavors: spatial and spectral. From what I can see the main difference between the two approaches is ...
3
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1answer
32 views

CNN how can i reduce gpu memory usage with large image sizes?

I am trying to train a cnn-lstm model, my sample image sizes are 640x640. I have a GTX 1080 ti 11GB. I am using Keras with tensorflow backend. Here is the model. ...
2
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1answer
50 views

What are the differences between Bytenet and Wavenet?

I recently read Bytenet and Wavenet and I was curious why the first model is not as popular as the second. From my understanding, Bytenet can be seen as a seq2seq model where the encoder and the ...
4
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1answer
95 views

What are the ways to calculate the error rate of a deep Convolutional Neural Network, when the network produces different results using the same data?

I am new to the object recognition community. Here I am asking about the broadly accepted ways to calculate the error rate of a deep CNN when the network produces different results using the same data....
3
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1answer
42 views

Can a model, retrained on images classified previously by itself, increase its accuracy?

Let's assume I have a CNN model trained to categorize some objects on the images. By using this model I find more categorized images. If I now retrain this model on data set that consists old set and ...
3
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1answer
34 views

GPU/TPU acceleration for neural networks with various network topologies

I was thinking about different neural network topologies for some applications. However, I am not sure how this would affect the efficiency of hardware acceleration using GPU/TPU/some other chip. If, ...
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3answers
871 views

What topologies are largely unexplored in machine learning?

Geometry and AI Matrices, cubes, layers, stacks, and hierarchies are what we could accurately call topologies. Consider topology in this context the higher level geometrical design of a learning ...
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0answers
13 views

Numbers to image regression

I would like to create a machine learnig framework that could predict the 3D heat distribution of a room(of size 120x120x120) , given multiple parameters(position of the heater, orientation, power of ...
3
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1answer
22 views

Image classification with an associated matrix

I have a dataset of images with 9 different classes. However, there are different categories with the same type of associated image and only can be differentiated with an associated matrix in my ...
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2answers
59 views

What are the major differences between cost, loss, error, fitness, utility, objective, criterion functions?

I find the terms cost, loss, error, fitness, utility, objective, criterion functions to be interchangeable, but any kind of minor difference explained is appreciated.
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2answers
49 views

How should we pad an image to be fed in a CNN?

As everyone experienced in deep learning might know, in an image classification problem we normally add borders to images then resize it to the input size of a CNN network. The reason of doing this is ...
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0answers
24 views

How to get same accuracy with identical models in Keras and Tensorflow?

As we all know Keras backend uses Tensorflow and so it should give out same kind of results when we provide same parameters, hyper-parameters, weights and biases initialisation at each layer, but ...
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1answer
72 views

Appropriateness of 3D Convolutional Neural Network for segmentation of medical image data

I have a couple different segmentation tasks that I would like to perform on medical imaging data using CNN's. I'm currently trying to wrap my head around how well a 3D network might work, using a U-...
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0answers
29 views

Get the position of an object, out of an image

I have some images with a fixed background and a single object on them which is placed, in each image, at a different position on that background. I want to find a way to extract, in an unsupervised ...
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0answers
7 views

Looking for the right type of 1D-Convolution that only considers one column/attribute

My input has the shape of n rows (time steps) and m columns (attributes). I want to train a convolutional neural network on it to predict a class. I am currently using 1D-Convolutions. I got a good ...
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0answers
10 views

How much data is needed to train a deep learning model to detect instance masks?

I am trying to get an idea of how much data is needed to train a deep convolutional neural network to detect instance masks from images. I am interested in both papers that have been written on the ...
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0answers
24 views

Threshold selection for Siamese network hyper-parameter tuning

I'm interested in modeling a Siamese network for facial verification. I've already written a simple working model that inputs feature vectors generated from two CNNs with shared weights then outputs a ...
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1answer
47 views

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

So, I'm using a pretrained pnasnet5large model to do some image classification (https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py) In the file, it ...
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1answer
30 views

How well can ConvNET distinguish an object from its class?

ConvNET can easily predict class of an object in an image. My question is, can ConvNET distinguish Pisa Tower from other buildings or Hagia Sophia from other mosquoes easily? If it can, how many ...
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1answer
130 views

Capsule Networks - Facial Expression Recognition

I want to experiment Capsule Networks on FER. For now I am using fer2013 Kaggle dataset. One thing that I didn't understand in Capsule Net was in the first conv layer, size was reduced to 20x20 - ...
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1answer
28 views

Tweaking a CNN for large number of input channels

I am using a CNN for function approximation using geospatial data. The input of the function I am trying to approximate consists of all the spatial distances between N location on a grid and all the ...
2
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2answers
445 views

How to architect a network to find bounding boxes in simple images

I have an application where I want to find the locations of objects on a simple, relatively constant background (fixed camera angle, etc). For investigative purposes I've created a test dataset which ...
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0answers
17 views

Matterport MRCNN and multiclass classification

I want to create a model which solve a multiclass classification problem. The main concept is: every picture contain only one object the background is very simple all object is coming from the same ...
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4answers
7k views

Convolutional neural networks with input images of different dimensions - Image segmentation

I'm facing the problem of having images of different dimensions as inputs in a segmentation task. Note that the images do not even have the same aspect ratio. One common approach that I found in ...
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2answers
51 views

What are the differences between network analysis and geometric deep learning on graphs?

Both of them deal with data of graph structure like a network community. Is there a big difference there?
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0answers
37 views

Which approaches are best suited for text deblurring?

I want to deblur text images using deep learning. Which approaches are best suited for the task? Any example networks? Is unsupervised network the best approach? GAN or cycle GAN for these purposes? ...
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0answers
16 views

Sliding Window Detection

Suppose that we have a labeled training set of $n$ closely cropped images of cars $(x_1, y_1) , \dots, (x_n, y_n)$. We then train a CNN on this. Let's say we have $m$ test images. Then for each of the ...
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1answer
44 views

Generate credit cards dataset for locating number region

Currently I'm working on a project for scanning credit card and text extraction from cards. So first of all I decided to preprocess my images with some filters like thresholding, dilation and some ...
9
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1answer
321 views

How much of a problem is white noise for the real-world usage of a DNN?

I read that deep neural networks can be relatively easily fooled (link) to give high confidence in recognition of synthetic/artificial images that are completely (or at least mostly) out of the ...
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0answers
19 views

Confidence Maps and Non-Linearity

I am currently trying to improve a CNN architecture that was proposed for generating depth images. The architecture was originally proposed for autonomous driving and it looks like following : The ...
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0answers
21 views

Reference request: one-hot encoding outperforming random orthogonal encoding

I experimented with a CNN operating on texts encoded as sequences of character vectors, where characters are encoded as one-hot vectors in one embedding and as random unit length pairwise orthogonal ...
3
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1answer
51 views

Is it useful to eliminate the less relevant filters from a trained CNN?

Imagine I have a tensorflow CNN model with good accuracy but maybe too many filters: Is there a way to determine which filters have more impact in output? I think it should be possible. At least, if ...
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1answer
43 views

Is there a theory behind which model is good for a classification task for the convolutional neural network?

Let say I'm trying to apply CNN for image classification. There are lots of different models to choose and we can try an ensemble, but given a limit amount of resources, it does not allow to try ...
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1answer
30 views

What does an oscillating validation error curve represent?

I have been training my CNN for a bit now and, while both the training loss and the training error curves are going down during training, both my validation loss and my validations error curves are ...
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0answers
13 views

Training, validation loss and accuracy yolov3?

This is a version of Yolo V3 implemented in PyTorch – YOLOv3 in PyTorch I am trying to use transfer learning to train this yolov3 implementation following the directions given in this post. This is ...