Questions tagged [convolutional-neural-networks]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
11 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 ...
1
vote
0answers
22 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 ...
4
votes
0answers
28 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 ...
0
votes
0answers
6 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 ...
0
votes
0answers
8 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 ...
1
vote
0answers
16 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 ...
-1
votes
0answers
31 views

Which opinions are available to slow down the DexNet robotic grasping project?

The first version of a robotic arm who is able to grasp objects dexterously was announced three years ago. [1] It is working with convolutional neural networks who are trained on thousands of 3d ...
3
votes
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 ...
1
vote
1answer
27 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 ...
0
votes
0answers
9 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 ...
1
vote
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 ...
0
votes
1answer
36 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 ...
1
vote
0answers
36 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? ...
2
votes
0answers
18 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 ...
1
vote
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
votes
1answer
48 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 ...
0
votes
1answer
57 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 ...
1
vote
1answer
28 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 ...
0
votes
0answers
12 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 ...
1
vote
2answers
25 views

How does one create a non-classifying CNN in order to gain information from images?

How do I program a neural network such that, when an image is inputted, the output is a numerical value that is not the probability of the image being a certain class? In other words, a CNN that doesn'...
1
vote
1answer
8 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
votes
1answer
38 views

Understanding the intuition behind Content Loss (Neural Style Transfer)

I'm trying to understand the intuition behind how the Content Loss is calculated in a Neural Style Transfer. I'm reading from an articles: https://medium.com/mlreview/making-ai-art-with-style-transfer-...
1
vote
1answer
33 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 ...
1
vote
1answer
17 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 ...
0
votes
0answers
18 views

Loss reduction, but constant performance with CNN

I made a CNN with a reasonable loss curve, but the performance of the model does not improve. I have tried making the model larger, I am using three convolutional layers with batch norms. Thanks for ...
1
vote
0answers
42 views

Torch CNN not training

I am completely new to CNN's, and I do not quite know how to design or use them efficiently. That being said, I am attempting to build a CNN that learns to play Pac-man with reinforcement learning. I ...
4
votes
1answer
25 views

Combining 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.
2
votes
0answers
26 views

3D geometry and similarity with a reference model

I am looking for a CNN method, or any other machine learning method, to recognize 3D natural geometries that are similar to each others, and compare these geometries with a reference 3D model. To ...
1
vote
0answers
40 views

CNN - Visualizing images near decision boundary - Pixels inexplicably tend to edges

We are exploring the images classified by a CNN at its decision boundary, using Genetic Algorithms to generate them. We have created a fine-tuned binary grayscale image classifier for cats. As the ...
2
votes
0answers
20 views

What is the difference between Squeeze-and-excite and bottleneck modules from Mobilenet v2?

Squezee-and-excite networks introduced SE blocks, while MobileNet v2 introduced linear bottlenecks. What is the effective difference between these two concepts? Is it only implementation (depth-wise ...
4
votes
1answer
57 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 ...
2
votes
2answers
50 views

Why do we get a three-dimensional output after a convolutional layer?

In a convolutional neural network, when we apply the convolution on a $5 \times 5$ image with $3 \times 3$ kernel, with stride $1$, we should get only one $4 \times 4$ as output. In most of the CNN ...
6
votes
1answer
33 views

Is there any use of using 3D convolutions for traditional images (like cifar10, imagenet)?

I am curious if there is any advantage of using 3D convolutions on Images like Cifar10/100 or Imagenet. I know that they are not usually used on this data set, though they could because the channel ...
1
vote
0answers
49 views

How to train and update weights of filters

I have some problems with training CNN :( For example: Input 6x6x3, 1 core 3x3x3, output = 4x4x1 => pool: 2x2x1 By backpropagation I calculated deltas for output. This tutor and other tutors are ...
1
vote
1answer
29 views

What is the difference between 2d vs 3d convolutions?

I was trying to understand the definition of 2d convolutions vs 3d convolutions. I saw the "simplest definition" according to Pytorch and it seems the following: 2d convolutions map $(N,C_{in},H,W) \...
1
vote
0answers
14 views

How do I recover the 3D structure of a layer after a fully-connected layer?

I want to implement a CNN, but I want to explore what happens when my first layer is a fully-connected one. I still want to use convolutions, of course, but I want to apply them after the first layer. ...
0
votes
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 ...
0
votes
0answers
19 views

Super Resolution CNN generates black dots on output images

I have been trying to train a CNN for the super-resolution task based on the work of Dong et al., 2015 [1]. The network structure built in PyTorch is as follows: ...
0
votes
1answer
54 views

What is the correct way to read and analyse images in machine learning?

I am trying to understand the best practice to read and analyze images. If your image has 10,000 pixels, your input layers will have 10,000 inputs? It sounds that my neural network will have too many ...
1
vote
1answer
48 views

What do the numbers in this CNN architecture stand for?

So I've got a neural net model (ResNet-18) and made a diagram according to the literature (https://arxiv.org/abs/1512.03385). I think I understand most of the format of the convolutional layers: ...
2
votes
2answers
47 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 ...
3
votes
2answers
40 views

Are fully connected layers necessary in a CNN?

I have implemented a CNN for image classification. I have not used fully connected layers, but only a softmax. Still, I am getting results. Must I use fully-connected layers in a CNN?
0
votes
2answers
40 views

Untrained CNNs as feature extractors?

I've heard somewhere that due to their nature of capturing spatial relations, even untrained CNNs can be used as feature extractors? Is this true? Does anyone have any sources regarding this I can ...
1
vote
0answers
17 views

Literature on Sequence Regresssion

I have some rated time-sequential data and I would like to test if an ANN can learn a correlation between my measurements and ratings. I suspect I could just try a CNN where 1 Dimension is time or an ...
1
vote
1answer
30 views

What loss function is appropriate for finding “points of interest” in a array of x,y inputs

I am looking into whether a neural network is appropriate to detect "points of interest" (POI) in a set of tuples (say length, and some sensor value). A POI is essentially a quick change in the value ...
4
votes
1answer
49 views

What is the difference between asymmetric and depthwise separable convolution?

I have recently discovered asymmetric convolution layers in deep learning architectures, a concept which seems very similar to depthwise separable convolutions. Are they really the same concept with ...
2
votes
0answers
29 views

Pipeline to Estimate Measurement of Human Body Point Cloud

I am developing a Body Measurement extraction application, my current stage is able to extract the point clouds of human body in a standing posture, from every angles. Now, to be able to recognize ...
1
vote
1answer
42 views

Are the training loss and validation loss plotted per sample or per batch?

I am using a CNN to train on some data, where training size = 21700 samples, and test size is 653 samples, and say I am using a batch_size of 500 (I am accounting for samples out of batch size as well)...
3
votes
2answers
132 views

When should I use 3D convolution?

I am new to convolutional neural networks, and I am learning 3D convolution. What I could understand is that 2D convolution gives us relationships between low-level features in the X-Y dimension, ...
4
votes
1answer
66 views

What is the meaning of “stationarity of statistics” and “locality of pixel dependencies”?

I'm reading the ImageNet Classification with Deep Convolutional Neural Networks paper by Krizhevsky et al, and came across these lines in the Intro paragraph: Their (convolutional neural networks') ...