Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

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Can we use CNN to effectively learn from a table whose various rows are permuted in the testing dataset, output for each remains same?

I have a set of classes, 37 to be precise. Each class is represented by a feature vector of size [1,10]. A single input sample has the dimensions of ...
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1answer
19 views

EfficientNet function composition or Hadamard

In the page 3 of the paper of EfficientNet, there is a equation $$\mathcal{N} = \bigodot_{i=1...s} \mathcal{F}_{i}^{L_i} \big(X_{\langle H_i, W_i, C_i \rangle}\big)$$ where $\mathcal{N}$ is the conv ...
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1answer
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PyTorchs ConvTranspose2d padding parameter

Im confused about what PyTorchs padding parameter does when using torch.nn.ConvTranspose2d. The docs say that: "The padding argument effectively adds dilation * (kernel_size - 1) - padding ...
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1answer
21 views

Debugging a simple 1-D CNN for solving a simple classification problem

I have a rather simple classification problem that I am trying to solve. Each instance in my problem is a list of 1024 bytes (each byte is represented by a digit between 0 and 255). There are 2 ...
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1answer
174 views

Convolution backpropagation

I'm in the progress to learn, and understand different neural networks. I pretty much understand now feed-forward neural networks, and back-propagation of them, and now learning convolutional neural ...
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2answers
86 views

Is Flatten() layer in keras necessary?

In CNN transfer learning, after applying convolution and pooling,is Flatten() layer necessary? I have seen an example where after removing top layer of a vgg16 ,first applied layer was ...
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1answer
15 views

Understanding declared parameters in my Conv2d layer of my convolutional neural network

I am trying to understand the architecture of my keras model implemented by the sequential model. Here is a piece of the code : ...
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0answers
11 views

Is it possible to create a 3d to 2d U-net?

I was curious if it is possible to create a U-net type architecture that takes in a 3d image and outputs a 2d image? Or, alternatively, would some other architecture be better suited for this problem? ...
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2answers
186 views

Why are results without Transfer Learning better than with Transfer Learning?

I developed a neural network for license plate recognition and used the EfficientNet architecture (https://keras.io/api/applications/efficientnet/#efficientnetb0-function) with and without pretrained ...
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37 views

ConvNet - What to improve regarding architecture, procedure and technique?

I have a dataset of 180k images of license plates (so, not necessary to localize the license plate at first) for which I try to recognize the characters on the images (License plate recognition). All ...
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24 views

Plotting scikit-learn confusion matrix returns no values in the last class

I am attempting to create a confusion matrix using Scikit-Learn for a multiclass classification CNN, and it works well except for the fact that it does not provide ...
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1answer
17 views

pytorch convolution with 0-stride along one dimension

For some square images, I'd like to use torch.nn.Conv2d with the kernel as a vertical block. As in, the kernel size is defined as max value of the first dimension by 1. Since the first dimension has ...
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1answer
26 views

Is always converting a input vector into matrix and apply cnn's good idea?

I know the benefits of using cnns(reduced size weight matrices). Is it a good idea to convert a input vector(which is not a image) into a matrix and apply cnn's. What I understand is that it should ...
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22 views

How to use AutoEncoders or VAE for initializing custom CNN architecture?

I would like to use AutoEncoder or VAE in order to learn set of features which I can use to initialize training procedure of a custom CNN architecture that I've build. Here is the code: ...
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Why does regression model predict the same output

I have built a CNN for regression, but it is giving identical predictions (up to 8 sig. fig.) for almost 1/3 of the test data set. (The other outputs are different.) Is there a reason why this might ...
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1answer
47 views

Back propagation through a simple convolutional neural network

Hi I am working on a simple convolution neural network (image attached below). The input image is 5x5, the kernel is 2x2 and it undergoes a ReLU activation function. After ReLU it gets max pooled by a ...
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34 views

1D convolutional neural network validation improvement

I created 1D CNN in Keras, but I'm having issues with validation loss and accuracy. I have 24k records, 22 features. Is my model overfitting or what is going on so validation loss and accuracy is ...
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8 views

How to use Meta-SGD with U-Net

I'm guessing how to use Meta-SGD with U-Net network. I've been searching for an example, but I haven't found anything yet. I'm reading the book Hands On Meta Learning with Python, which has the ...
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3answers
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How does “ Sparsity of connections” in CNNs causes the network to have less parameters?

I am studying Andrew NG's lectures on Convolutional Neural Network and he had provided two reasons for CNNs having less parameters compared to Non-Convolutional networks . They are : Parameter ...
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15 views

Configuration of CNN model for recognition of sequential data - Architecture of the top of the CNN - Parallel Layers

I am trying to configure a network for character recognition of sequential data like license plates. Now I would like to use the architecture which is noted in Table 3 in Deep Automatic Licence Plate ...
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29 views

How to deal with severe overfitting in a UNet Encoder/Decoder CNN in a task very similar to image translation?

I am trying to fit a UNet CNN to a task very similar to image to image translation. The input to the network is a binary matrix of size (64,256) and the output is of size (64,32). The columns ...
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1answer
22 views

Varying Image sizes in Tensorflow Malaria dataset | Dealing with unclean tensorflow data

I am trying to build a CNN based image recognition system for the Tensorflow malaria dataset. I loaded the dataset (~27k RGB images) using conventional tensorflow_datasets syntax. After some data ...
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Local RoI features in Faster/Mask R-CNN

I use Faster and Mask R-CNN for various problems, including training GANs. I'm particular interested in local features that Region Of Interest module in Faster R-CNN extracts from feature layer(s). ...
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Segmentation-free character recognition on an image: Multi-label, multi-class or sequential image classification problem?

I have some images which look like this one: They exist of 3 possible characters (A-C) and a length of 4. Now, I would like to run a neural network, which recognizes each character in the picture ...
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1answer
20 views

CNN model contains several images that are null

I'm using a deep CNN with the ReLU activation function. When visualizing the layers (each with 32 filters), several of the filtered images are zeros. I am trying to reason why this may be happening? ...
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1answer
40 views

What is the best way of combining audio and visual data to make predictions?

I am trying to predict the probability of a disease by using audio and images, the audio and the images do not come from the same source. I am thinking of combining the outputs (maybe average them) of ...
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18 views

Definitive Values in Confusion Matrix

I built a convolutional LSTM model for the classification of 4-image time series. I used n keras ConvLSTM layers, followed by a time-distributed flatten and a few dense layers, finalized by a dense ...
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3answers
150 views

Disparity between training and testing errors with deep learning: the bias-variance tradeoff and model selection

I am developing a convolutional neural network and have a dataset with 13,000 datapoints that is split 80%/10%/10% train/validation/test. In tuning the model architecture, I found the following, after ...
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7 views

Common sub-networks

I am pruning a neural network (CNN and Dense) and for different sparsity levels, I have different sub-networks. Say for sparsity levels of 20%, 40%, 60% and 80%, I have 4 different sub-networks. Now, ...
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28 views

How to interpret Deep learning network architecture into a diagram?

How to draw this Deep learning network architecture diagrams? I'm using Faster R-CNN: R50-FPN. Any ideas or tip to convert this to a diagram? Or just to know which are input, hidden and output ...
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12 views

AI architecture for time and spacial sequences

I am working on a project where I analyse MEG data. I have 102 channels as a vector and a 2D matrix of the channels (11x14) to show spatial relations - I want to include that in the AI architecture. ...
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50 views

Gender Prediction from Offline Handwriting Using Convolutional Neural Networks

Starting from the fact that handwritten documents style are gender-dependent (male and female have different writing styles), I'm trying to predict writer's gender from its handwritten scripts using ...
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Annotating images for CNN; how to label partially obscured images

I am annotating images to train a CNN classifier, some images are partially obscured, generally speaking what is the intuition and advice in these situations, should a partially obscured image be ...
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1answer
11 views

Unstable results in test mode with fractional max pooling in PyTorch

I make some variants of ResNet, originally found in TorchVision, modify them, train them and so on. What I have found is that even in .eval() mode, even if I load state right before evaluation, I ...
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10 views

How to download and see the actual model-creation script for the ResNEXT model at this github?

I'm new to using python/scripting and AI. I was looking into https://github.com/facebookresearch/semi-supervised-ImageNet1K-models that describes ResNEXT (variant of Resnet). Clicking on ...
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Using a 3-D convolutional layer to simulate a 2-D convolutional layer

I've asked this question on the AI StackExchange , but I received no insight so I'd like to ask it here. Is using a filter of size (1, x, y) on a 3-D convolutional layer the functionally equivalent ...
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0answers
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What is the meaning of 'concatenate' in this neural network architecture?

I am trying to understand the lane edge proposal network proposed in LaneNet for lane detection. My understanding of this is that a number of convolutional and pooling layers are first used to ...
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0answers
13 views

The idea behind Generalized Max Pooling

I am trying to understand the idea of "Generalized Max Pooling". It seems they try to make the 'pooled' representation similar to the features. If so I feel some rare discriminating features could ...
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9 views

Best model structure for input coordinates producing full images

I am trying to produce a machine learning model that works as an interpolator of real data. Essentially what would go into the model is an xy coordinate the result ...
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21 views

Free dataset to train a neural network in order to extract text from image

I'm building a custom OCR to recognize and get text from png image. In order to dealing this task, i'm using python with tensorflow library (1.14.0) to develop a Convolutional Neural Network that ...
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78 views

Imblanced-data: Need assistance with SMOTE technique for a CNN input

I am trying to apply the SMOTE sampling technique to over-sample the minority class of a multiclass (5-class) problem using the convolutional neural network. As far CNN requirement, the input shape ...
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178 views

Keras CNN model gives no gradients error during training

I’m trying to create a Convolutional Neural Network model, using an 824 image dataset, for predicting an output value. Problem is that the dataset is quite unstructured, as there are plenty of RGB and ...
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0answers
19 views

Preparing text for modeling in dialogue structure

I'm working on implementing the DialogueGCN code from this paper. Its a model that classifies the 'emotion' from utterances of text within a conversation. As this model takes into account speaker ...
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39 views

What does “full connection table” mean in Yan LeCuns comment on 1x1 convolutions?

What does "full connection table" mean in Yan LeCuns comment on 1x1 convolutions? In Convolutional Nets, there is no such thing as "fully-connected layers". There are only convolution layers with ...
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1answer
48 views

UNet Model accuracy is stuck at exact 0.5 (neither more or less) (No class imbalance, tried tuning learning rate)

This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. Loss does decrease. I have also checked for class imbalance. I have ...
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25 views

Dealing with low variation data

So my current project involves using a neural network to try and predict the probability of a player getting a kill in a first-person shooter. I've recorded a number of features that should be ...
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What happens with activations?

I am playing with convolution network, assembling something between AlexNet and ResNet. Not very deep, about 10 conv. layers including 2 through residual connection, and 3 fully-connected layes at the ...
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2answers
32 views

What can we understand from max-activation generated images?

There are several approaches to generate psychedelic images, providing maximum activations for individual neirons in convolutional neural networks. For example there is a lot of them there https://app....
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1answer
141 views

How to combine different models in Keras?

I have a pre-trained network, consist of two parts, the feature extraction, and the similarity learning. The network takes two inputs and predicts the images are same or not. The feature extraction ...
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163 views

How to detect vanishing and exploding gradients with Tensorboard?

I have two "sub-questions" 1) How can I detect vanishing or exploding gradients with Tensorboard, given the fact that currently write_grads=True is deprecated in the Tensorboard callback as per "un-...

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