Questions tagged [cnn]

Convolutional Neural Networks (CNN, also called ConvNets) are a tool used for classification tasks and image recognition. The name giving first step is the extraction of features from the input data.

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Validation Accuracy remains constant

I am training a model for image classification, my training accuracy is increasing and training loss is also decreasing but validation accuracy remains constant. Here is my code: from keras....
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Visualization of how K means clustering is used to select K anchor boxes?

I was having trouble understanding how K means clustering is used to select K anchor boxes. We have ground-truth boxes and we run K means clustering on them using IOU as a metric. There is no good ...
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Recursive Transfer Learning

Is there any methodology called Recursive Transfer Learning? For example, let's consider a situation that we have a lack of data while training a convolution neural network (CNN) for object detection ...
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Feature-to-parameter mapping in neural networks

For neural networks, can we tell which parameters are responsible for which features? For example, in an image classification task, each pixel of an image is a feature. Can I somehow find out which ...
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Error with Input shape in keras [migrated]

I am using Keras to build a CNN and I have the following code snippet: ...
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Storing outputs from convolutional layers

I am working on a CNN architecture over video clips that attempts to separate temporal and spatial features, inspired by this paper. The relevant architecture is on Figure 5(b). Here is my idea: I ...
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How gradients are learned for pooling layers in Convolution Neural Network?

Assuming we could compute a layerwise Hessian of the error function when training a neural network, the error sub-surface of pooling layers will be flat.?? Is that correct? There is no weights to be ...
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Detecting features in XY Plots using CNNs

I have a simple classification problem - if two features plotted on a simple XY plot show a "kink" or characteristic turn, then the label is TRUE, otherwise FALSE. I've been attempting to detect the "...
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specifics of dilated convolutions in tensorflow

This ones a little esoteric. I'm developing a wave-net inspired model. First time needing dilated convolutions. My question is which values is the convolution looking at relative to the output ...
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Does someone have a low level inplemenation of tensorflow's ConvLSTM2D

Right now I am using the ConvLSTM2D module of tensorflow and it works. But now I would like to feed an additional input between the convolutional stage and the lstm module. Therefore I would need a ...
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How to remove layers from a TensorFlow2 model?

I have a CNN model which has a lambda layer doing One-Hot encoding of the input. I am trying to remove this Lambda layer after loading the trained network from a h5 file. So far I have tried to create ...
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Can a convolutional neural network or an autoencoder deal with an input of complex values (complex numbers instead of real numbers)?

I saw in a model that they did consider the complex numbers as 2-D numbers before using Convolutional Neural Networks. However for the autoencoder, as much as i know, it can not deal with 3D, Am i ...
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Is it possible to train a model on a more complicated data set and then retrain on a simpler dataset that has continuous frames for LSTM model?

I'm attempting to do lane detection using CNN-LSTM architectures on the TuSimple dataset. However the TuSimple data set isn't very difficult and the results are sometimes poor (visually). I want to ...
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How to extract the reason behind a prediction using TensorFlow?

I created a CNN using TensorFlow2 and trained it as a binary classifier. Is there a way to extract the influence of each pixel upon the prediction? I am trying to obtain a mask similar to the ...
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Conv1D specify output chanels in tensorflow 2.1

Hello I'm trying to implement the "Tacotron Towards end to end" paper and in the Encoder CBHG - a Conv1D bank of K=16, conv-k-128-ReLU “conv-k-c-ReLU” - denotes 1-D ...
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How will a CNN for object detection learn from some improper annotations?

For example I want a FasterRCNN to detect dogs and cats in images. I have a dataset of 100 images. In each image there is atleast one dog and one cat (both classes are present in all images). But ...
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Tensorflow CNN not predicting correctly for a well trained model

So I have built a CNN and trained it to around 95% accuracy on training data, and 90% on testing data. The issue is, when I save this model, load it in again, and predict, it always predicts 1 or ...
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Convolution v.s. Cross-Correlation

I understand that from mathematical point of view, only difference between Convolution and Cross-Correlation is that Convolution is commutative, while Cross-Correlation is now. In many articles ...
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Validation Accuracy greater than train accuracy, validation loss lesser than training loss MTL

I am training a multi task model using VGG16. Datase: Dataset contain 11K images. There are two tasks: The dataset is imbalanced, 1) PFR classification: 10 classes 0 --- 5776 10-12 --- 1066 6-...
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What is Quantum Convolutional Neural Network?

I just came through the TensorFlow Quantum library they introduced Quantum Convolutional Neural Network. What is Quantum Convolutional Neural Network? What is the difference between CNN vs QCNN?
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ImageDataGenerator for multi task output in Keras using flow_from_directory

I am creating a multitask CNN model and I have two different classification properties (one with 10 classes, 2nd with 5 classes) and my directory structure looks like this: ...
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How to make a better regression(a lot of element is zero, try to regress nonzero elment acurrately)

I have a picture to picture regression problem. I use a fully connected convolutional neural network to regress. The output picture only has numbers bigger than zero in the four corners and center, ...
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How to create a feature vector given final set of feature maps?

I've got a faster-rcnn (resnet-101 backbone) for object detection, and am extracting feature tensors for each detected object, which is a 7x7x2048 tensor (basically 2048 feature maps, each 7x7). For ...
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Relationship Between Anchors and the Kernel in Faster R-CNN

In the Faster R-CNN paper, namely for the RPN section, it is mentioned that a 3x3 kernel is run over the input feature map to create a 256-channel Conv layer, which is then convolved by a 1x1 kernel ...
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In Fast R-CNN how are input RoIs mapped to the respective RoIs in the feature map before RoI pooling?

I've been reading the Fast R-CNN paper My understanding is that the input to one forward pass is the whole input image plus a list of RoIs (generated by selective search or another region proposal ...
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Keras: DepthwiseConv3DTranspose or doing transposed Conv. with a Conv. layer

I am building an autoencoder for 3D images and would like to use Depthwise convolutions. For the encoder, I found an implementation of a depthwise 3D convolutional layer (DepthwiseConv3D). For the ...
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1answer
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Intensity image to RGB for transfert learning

My goal is to use a pre-trained model with intensity based image. Most pre-trained model expect RGB (int) format as input image. An easy workaround is to dupplicate the intensity channel 3 times in ...
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Ensemble classifier combine cnn-svm features

For text classification output of the max-pooling layer of CNN classifier goes to a flatten layer and then to a dense layer with ReLU activation. On the other hand output of SVM is passed through a ...
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Creating a custom dataset for object detection

I'm currently trying to build a model to recognize approximately 10 labels (food items) in a fairly controlled environment (refrigerator). I was unable to find datasets that worked well enough for my ...
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How to use Keras predict_generator() for segmentation output?

Below is the code I'm using for segmentation mask prediction after using fit_generator(...) on model named m :- ...
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CNN - Confused on the output shape of second convolutional layer

I'm attempting to write a forward pass of a CNN but I'm stuck on the second convolutional layer. From what I understand, given an image of size 28x28, a first filter of size 10x3x3, a second filter ...
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Language translation with convolutional neural network

Many examples of language translation neural networks: "the cat sat on the mat" -> [model] -> "le chat etait assis sur le tapis" use RNN, and in particular LSTM. See for example Sentences language ...
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What is the human performance on ImageNet, the top 1 error? (not top 5 error)

What is the human performance on ImageNet, the top 1 error? (not top 5 error). The best of the machine learning algorithm top 1 error is 11.6% ,according to thie website https://paperswithcode.com/...
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When should I use YOLO/MaskRCNN instead of a Vanilla CNN?

I understand how YOLO and other object detection networks work but also see some people using a simple CNN to predict a point / bounding box. When should I use something more complex? If I’m just ...
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i have trained a model using fer2013 dataset using CNN for Emotion detection. Now i want to use it in a image

I have a trained model and saved the weights in fer.h5. Now i want to use the pre trained model in another set of images and save it to a excel file ...
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Is it possible to create a neural network with two inputs, with sequential layers?

Is there a natural way, in terms of structure of the layers of a NN, in order to pass 2 inputs vectors to the NN? Example: text authorship identification Input #1: sentence1 by unknown author ...
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1answer
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Convolutional neural network giving high confidence on wrong classification

I am doing a project on sign language recognition through CNN. My Dataset 300 images for each alphabet and one special symbol for space. Images are taken from three individuals. Each image has hand ...
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How to Increase Low Accuracy Keras Model Design?

I am trying to use the Embedding layer with CNN. Also, use train data 1200 and test data 300. use Keras model design. At this moment, the accuracy achieved 40% or 50%. For this reason, My Question is ...
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Multimodal end-to-end deep learning

I'm thinking of working on a project that involves multiple models of data and wanted to share my thoughts to get some feedback. Think of problem of sentiment classification where the input contains ...
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Conv-2 CNN architecture - CIFAR-10

I have a CNN architecture for CIFAR-10 dataset which is as follows: Convolutions: 64, 64, pool Fully Connected Layers: 256, 256, 10 Batch size: 60 Optimizer: ...
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1answer
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Finding the appropriate CNN Model Architecture and Parameters

I am currently creating a CNN model that classifies whether the font is Arial, Verdana, ...
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Multimodal vs recurrent network for video-based FER

I am wondering what types of architectures are best to explore for doing video-based facial emotion recognition. Some application feature using an architecture spatial-temporal architecture of 2 CNNs, ...
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Getting big losses and little accuracy on image classification model with cnn

i am currently working on image classification of artworks from this site https://www.kaggle.com/ikarus777/best-artworks-of-all-time and following the tutorial from this site https://...
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How are the channels handled in CNN? Is it independently processed or fused?

Let's assume that we are talking about 2D convolutions applied on images. In a grayscale image, the data is a matrix of dimensions $w \times h$, where $w$ is the width of the image and $h$ is its ...
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EfficientNet: Compound scaling method intuition

I was reading the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks and couldn't get my head around this sentence: Intuitively, the compound scaling method makes sense ...
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1answer
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Keras BatchNormalization axis

I use spectrogram as input to a Convolutional Neural Network I have created with tensorflow.keras in Python. Its shape is (time, frequency, 1). The input's shape of the CNN is (None, time, ...
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Fusing batch normalization with deconvolution in neural networks

I am trying to raise the performance of my convolutional neural network and for that reason I am trying to implement batch normalization fusing. Things are fine when I use fuse with convolution layer,...
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How to recognize overlapping digits?

I've got a set of images with overlapping digits which need to be recognized. The task seems a good fit for neural networks but the issue is that they are used to have inputs as single digits but in ...
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Is it possible to fit GMM so that mean lies outside of image bounding box?

I m currently reading https://arxiv.org/pdf/1411.3159.pdf They take gradient maps and fit Gaussian Mixture Model. Then they evaluate how often is the activation center inside of the image bounding box....
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How to represent and work with the feature matrix for graph convolutional network (GCN) if the number of features for each node is different?

I have a question regarding features representation for graph convolutional neural network. For my case, all nodes have a different number of features, and for now, I don't really understand how ...

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