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Questions tagged [convolution]

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Notation in “Holistically-Nested Edge Detection”

I am reading the paper "Holistically-Nested Edge Detection ". I will refer to it by abbreviation Hed. I don't get what the authors have in mind, when they denote certain convolutional layers in ...
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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
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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
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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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25 views

Keras TimeSeries - Regression with negative values

I am trying to make regression tasks for time series, my data is like the below, i make window size of 10, and input feature as below, and target is the 5th column. as you see it has data of {70, 110, ...
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7 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
17 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
6 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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29 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
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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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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) ...
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1answer
33 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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21 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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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
97 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: ...
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22 views

target and logits in the Mask-RCNN

I tried to implement the loss function in the mask-RCNN model using the Tensorflow tool. I used the average sigmoid cross entropy loss function: ...
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1answer
73 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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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 ...
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69 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
34 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 ...
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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 ...
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1answer
22 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'...
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2answers
52 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 ...
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0answers
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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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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 ...
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1answer
130 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 ...
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1answer
263 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
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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
23 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 ...
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1answer
605 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? ...
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1answer
35 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
133 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 ...
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1answer
112 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..
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1answer
29 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
33 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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141 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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1k 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 ...
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1answer
198 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 ...
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1answer
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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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146 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 ...
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1answer
27 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, ...
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1answer
151 views

Spatial Transformer Networks vs Deformable Convolutions

As I understand STN as described by the the deepmind paper https://arxiv.org/abs/1506.02025 allow a neural network to learn how to perform spatial transformations on the input image in order to ...
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2answers
80 views

How to determine if problem is solvable by neural network?

I want to identify subtle patterns in images using a convolutional neural net. I have seen several examples where people gave up reasoning that the pattern is not dominant or consistent enough to be ...
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1answer
591 views

Reshape output of convolutional layer to which dimensions?

I have a convolutional network, taken from this github, the one in build.py. Because it was made in an outdated version of keras I am trying to rewrite this to ...
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2answers
701 views

Convolutional Neural Networks layer sizes

I am trying to understand an article Backpropagation In Convolutional Neural Networks But I can not wrap my head around that diagram: The first layer has 3 feature maps with dimensions 32x32. The ...
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1answer
309 views

Convolution neural network: small dataset affecting accuracy

I have dataset of 36 folders 1 image each(total 36 images) the dataset is too small but these are character images which i want to train my val_acc= 0.0229 and <...
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0answers
48 views

how much GPU RAM is needed for Locally-connected layer

I want to implement DeepFace, which is very successful architecture in face recognition,in DeepFace network they used ...
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1answer
402 views

Convolution over volume in CNNs

I have a simple question about convolution layers in CNN, consider that we have 32 features map with size 100x100 so can we set 16 convolution layer with size 9x9x16 after features map? there isn't ...
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1answer
747 views

Tips & Tricks on training DCGAN on small dataset

I have made a DCGAN which I am trying to train on custom dataset of only 1200 images. I have tried to gather more, but even gathering these 1200 was hard enough. If you are wondering I used Google ...