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Questions tagged [convolution]

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1answer
16 views

Training a floor detection model: use full room images or only the cropped floor?

I'm trying to build a floor type image classification model.There's an open dataset called OpenSurfaces containing images segmented by the material type of every item appearing on a room. Something ...
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3answers
19 views

How to classify images Neural Network didn't trained to Understand

Let's say I trained a Convolution neural network to Identify Cats , Dogs and wolves . But suddenly I feed it pictures of rabbits and Lions. so how can I classify those as pictures as "Other" I ...
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1answer
15 views

How to add more emphasis on end columns in CNN?

I am using CNN for multivariate time series analysis. The input size is (batch_size, 500, 30) i.e 30 variables and 500 time steps. I want to put more emphasis on recent data and less on past data. ...
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1answer
18 views

Wrangling data for CNN

I am using convolutional nets for a physics application. I am trying to figure out how to structure my raw data as an image for input into the CNN. I have $N$ samples. Each sample consists of the ...
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0answers
28 views

Understanding how to use ConvLSTM for multistep ahead forecasting

I have a problem where I have transaction data for many banking accounts. The task is to train a model on historical debit/expense transactions and then forecast expense transactions for the next n ...
3
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2answers
103 views

Tensorflow (or Keras) vs. Pytorch vs. some other ML library for implementing a CNN [closed]

I am looking into implementing a convolutional neural network for a research problem. I've heard of deep learning libraries like Pytorch and Tensorflow and was hoping to get some additional ...
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0answers
15 views

Implementation of Inception-Resnet V2 shapes does not match

I am trying to implement the Inception-Resnet V2. In the original paper the authors outlined the network as in the figure below: One can say that the output dimension of the earlier block is the ...
2
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1answer
31 views

How does Pooling Layer in CNN introduce invariance to other transformations besides translation

Here is a quote from deeplearningbook which I am trying to process. I am not sure what do they mean by this quote, can someone help me understand please? Pooling over spatial regions produces ...
3
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2answers
70 views

What is the purpose of a 1x1 convolutional layer?

SqueezeNet uses 1x1 convolutions. I try to understand it in this simple example: if the input is one MNIST digit, i.e. of shape 1x28x28x1 (I use Batch x Height x ...
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0answers
13 views

Training on continuous data for profile regression

I have a large data set consisting of millions of 1-dimensional profiles. The profiles themselves are arbitrarily complicated continuous functions, $f(x)$, each bound from $0 < x < 1$. These ...
0
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1answer
14 views

Subsequent convolution layers

Note: I've read How do subsequent convolution layers work? a few times, but it's still difficult to understand because of the parameters $k_1$, $k_2$, and many proposals (1, 2.1, 2.2) in the question. ...
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0answers
8 views

Test Loss plateau fast in Convolutional Neural Net

I have a 10k dataset of 1 channel 100X100pixels images with 31 classes. I set up a CNN with 3 convolution layers each followed by a batchnorm and a 2d pooling. I tried out several combinations of ...
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1answer
21 views

YOLO pretraining

I'm implementing YOLO network and have some questions. In the original paper the authors say: "For pretraining we use the first 20 convolutional layers from Figure 3 followed by a average-pooling ...
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1answer
39 views

YOLO layers size

According to the original paper, the input size of the YOLO network layer is 448x448x3 and after the filter (7x7x64-s-2) is applied the output shape is to be 221x221x192 as I suppose. Some sources ...
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0answers
14 views

Implementing lookahead convolution - Tensorflow

I am trying to implement this research paper in python using Tensorflow. I am stuck at the lookahead convolution part. I have defied a weight matrix of shape ...
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1answer
31 views

Print the prediction of convolutional neural network

I have designed a convolutional neural network using tensorflow which looks as follows ...
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0answers
66 views

Why does Position Embeddings work?

In the papers "Convolutional Sequence to Sequence Learning" and "Attention Is All You Need", positions embeddings are simply added to the input words embeddings to give the model a sense of the order ...
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0answers
32 views

Question about Network Pruning from Deep compression paper by SongHan

For this Deep Compression paper by Song Han , I have few questions regarding the Network Pruning section. How does the math expression 2a+n+1 come around ? How does "store the index difference ...
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2answers
29 views

Extracting Useful features from large convolutional layers

I have been training a convolutional neural network on emotion detection. Now, I would like to extract features for my data to train an LSTM layer. In my case, the top convolutional layers in the ...
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0answers
14 views

Why is this convolution equation easier to apply than it's commutative counterpart?

The convolution is an operation on two functions of a real- valued argument. The convolution operation is typically denoted with an asterisk: s(t) = (x ∗ w)(t) ...
0
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1answer
138 views

Convolution and Cross Correlation in CNN

What would be the intuition behind using the convolution and cross correlation operation inside Convolutional Neural Networks? I am interested in putting together the theory from Digital Image ...
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0answers
32 views

How exactly do Convolution and Pooling act as infinitely strong prior? A mathematically written explanation will be very useful

In “Deep Learning” book by Goodfellow, Bengio and Courvile. P. 354, sec 9.4, they state: "convolution and pooling as infinitely strong prior". All I understood from here is that since a convolutional ...
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1answer
99 views

Combining 2 Neural Networks

2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ...). ...
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1answer
420 views

Keras Conv1D for simple data target prediction

I am trying to use conv1D layer from Keras for predicting Species in iris dataset (which has 4 numeric features and one categorical target). Following is my code: ...
10
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1answer
171 views

What is the difference between upsampling and bi-linear upsampling in a CNN?

I am trying to understand this paper and am unsure of what bi-linear upsampling is. Can anyone explain this at a high-level? https://arxiv.org/abs/1606.00915
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0answers
74 views

Preserve colour in convolutional autoencoder

at the moment i work with convolutional autoencoder and now I'am looking for paper or methods that adresses a colour preversation. Most of the AE paper use grayscale images and loss functions such as ...
2
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0answers
103 views

Transpose convolution math not working out

I was reading A Guide to Convolutional Arithmetic to understand Transpose Convolution as it is cited in Keras and Theano documentation. I am having trouble understanding the following two statements : ...
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2answers
40 views

Seemingly good results with training a CNN but bad when testing

I have an image classification task, and I am using Keras for a network with CNN layers, with what seems like good results in training, translates to poor performance in testing. Upon training, I ...
2
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0answers
162 views

Matlab: setting static iterations per epoch in a CNN

I'm building a convolutional neural network using Matlab's neural network toolbox. I have code designed to cross-train the network with different data sets, using the previous network's layers in ...
3
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1answer
24 views

Isn't the depth of a convolutional layer, the number of colors (or colorspace size)?

I have been going through a CNN tutorial and noticed that depth of a convolutional layer is equal to the number of filters. But, shouldn't the depth be the number of colors in the image? I mean, if it'...
2
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2answers
86 views

understanding the filter function in convolution neural network

I am trying to follow following tutorial accessible with This Link Under 3rd Heading, "3. Visualize the Activation Maps for Each Filter" we can see the following function ...
2
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0answers
19 views

How does convolution operation perform in CNN?

I know that convolution operation has the property, associative law. if I need to use multiple filters in succession and to perform this operation on multiple images, it makes sense to convolve the ...
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0answers
34 views

Random Training set for GAN's [closed]

I have studies the gans in depth and some of its type like cycle, pix2pix, cgans. Now I want to generate random images from random distribution from generator. So I am creating a dataset with no ...
2
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1answer
208 views

What is meant by 'training patch size'?

Currently I read a paper about symmetric skip connections for autoencoder (link). One experiment of them changes the the 'training patch size'. In my understanding patches are sub-boxes of an image ...
0
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1answer
386 views

Adding bias in deconvolution (transposed convolution) layer

My question is regarding the transposed convolution operation (also commonly called deconvolution or upconvolution). In TensorFlow, for instance, I refer to this layer. My question is, how / when do ...
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0answers
25 views

ConvNet exploding/vanishing loss

Problem I am using Fully Convolution Network (FCN), to classify whether an image is a random noise or not. However my FCN always predict with the means of targets. the output is not stale, but only ...
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2answers
25 views

Validation set performance increased, test set performance decreased

I am training a CNN model for a three-class classification problem. To do this, I'm gradually unfreezing more convolutional blocks of a pre-trained Resnet-18 network. The thing is that after ...
3
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1answer
1k views

GAN vs DCGAN difference

I am trying to understand the key difference between GAN and DCGAN. I know that DCGAN uses a convolutional network. But: What data is better to push into GAN and what data fits better to DCGAN? ...
2
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1answer
38 views

Why don't convolutional computer vision networks use horizontally - symmetric filters?

If, for example, I have a neural network for classifying dog breeds, and I feed it an image of some dog, inherently it shouldn't matter whether I feed it the original image or the image, mirrored ...
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0answers
170 views

How to properly represent a tic tac toe board to a CNN?

I'm figuring out how to manipulate convolutional neural networks (CNN) in python and I want to apply this kind of NN to an agent player that plays tic tac toe. I know that's weird and the problem ...
1
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1answer
121 views

Can I install Tensorflow in Anaconda without using Keras?

Can I install Tensorflow in Anaconda without using Keras? If I can what is the difference between using Keras with Tensorflow and only Tensorflow? Thanks..
2
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1answer
34 views

How to determine the number of forward and backward passes in deep learning (CNN)? [closed]

Is there a way to determine the number of forward and backward passes in the training of a neural network using python?
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0answers
44 views

Low loss but really bad results

I am training object detection using CNN I am using MSE as loss functions, at best it throws loss around 1800 on testing and 1850 on training data, Which in my opinion is quite acceptable, ...
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0answers
165 views

What is the difference between feature extraction and feature representation in deep learning?

What is the difference between feature extraction and feature representation in deep learning (CNN)? Thank you
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0answers
2k views

Multi-label classifciation: keras custom metrics

Contextualization I am working on a multi_label classification problem with images. I am trying to predict 39 labels. In other words, I am trying to identifying which one of the 39 characteristics is ...
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1answer
29 views

Using small CNN for de-noising on a full image

I have trained a CNN, in Keras, to remove noise from an image, the input shape is (5,5) and I trained it by using patches from an image with noise with the expected output for the center pixel. To ...
0
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1answer
329 views

I still don't know how deconvolution works after watching CS231 lecture, I need help

https://www.youtube.com/watch?v=ByjaPdWXKJ4 This is the time-stamped video of "deconvolution". I can understand normal convolution but not so much with upsampling convolution. In the video he ...
2
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1answer
98 views

What are the state-of-the-art models for identifying objects in photos?

From my observations and little experience it appears that most of the ML project are about classifying stuff. Is there cancer signs on the photo? Does the picture show car, whale or banana? Etc. I ...
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0answers
158 views

RNN-CharCNN for sequence tagging: convolution best practices

I've got an LSTM sequence tagger - say, for NER in BIO-tag format, but it might be used for different purposes in the future. I'm extending it with a Char-CNN for better OOVs processing and better ...
1
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1answer
34 views

tuning a convolution neural net, sample size

I keep reading that convolution neural net (CNN) performs best with lots and lots (100k+) of data. Is there any rule of thumb, or lower limit for data size during the grid search phase? For example, ...