All Questions
Tagged with conv-net or convolutional-neural-networks
1,196 questions
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How to resize the time-frequency spectrum of 1D signal so that image classification model can be used?
In case of 1D signal generally they tend to be around 1000s of sample points for each trial especially for biomedical signal. The time-frequency spectrum then can have shape like [256,1000] or [50,...
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64
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How to train a model to make predictions for larger sequences than those in the dataset?
I'm working on a project where I need my model to predict a sequence of n 3x3 matrices given an input sequence of n 3x3 matrices ...
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1
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31
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why CNN can still classify permuted images (originally multi-channel EEG data)?
I used multiple images to do classification. These images are multi-channel EEG data. Now I used the same random matrix to shuffle all elements on the rows and columns. I use CNN to do classification ...
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53
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How to overcome overfitting when you can't increase the training data due to constraint in time and resources?
I trained a complex cnn but when I look at the training and validation loss, it seems that the model is overfitting. But due to limitation on resources I can't increase the datasize. Is there any ...
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42
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Teaching a neural network when order of output does not matter
I am trying to train a neural network that predicts 2 3D points in space by looking at the hit counts from voxel data. I am currently doing this by making the network return 8 floats (x1,y1,z1,x2,y2,...
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17
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Detect moving persons by analyzing pair of images
Consider a case where you want to detect moving persons. No animals exist at the place.
The illumination at the place may vary a lot.
You don't have to detect immobile humans.
A camera produces a ...
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18
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How can I visualize pixel level information perceived by a model (VGG-16)?
As a fast.ai starter project I regressed a little model on movie stills from various eras to see if I could predict a year of release given an unseen image from a film. It works reasonably well! (feel ...
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29
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How to analyze furniture for digital reconstruction?
My end-goal is to take a single photo of a piece of common furniture (couch, chair, table) and create a 3d model from that. I'm a novice with deep learning as I've only done basic CNN's and such with ...
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19
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My CNN validation Accuracy increases super slow?
im doing a retinopathy detection project with over 3500 images, 700 in each class. I've filtered the image like
It seems that my model isn't learning from the data, or is having trouble because the ...
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19
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Efficient Net V2 M ONNX model infers significantly slower on small input
When I convert an Efficient net v2 m model from Pytorch to Onnx on differently sized inputs, I notice a strange and unexplained behavior. I was hoping to find an explanation to my observations from ...
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2
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64
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How do I improve my model accuracy and val_accuracy for my cnn model?
I'm using 3000+ retinopathy images in my CNN model. The accuracy remains around 77 to 80, how do i improve the accuracy value and reduce loss value?
I've tried dropout and Adam optimizer to increase ...
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28
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How to Handle Masked Values in Neural Networks for Geospatial Data?
I am working on neural networks for oceanographic data and face challenges in dealing with masked values, which I set to NaN.. I can train a neural network model with 1D vertical profiles (e.g. ...
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46
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Early divergence of YOLOv7-tiny train and val obj_loss plots
I am training a YOLOv7-tiny model and have the following observations from the training session:
the train and val objectness loss plots diverged pretty early on in the training process
the class and ...
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35
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What is the difference between 3 separate 5x5x1 convolutions, and one 5x5x3 convolution?
The actual numbers are just for the sake of clarifying my question, of course. What I mean is, since each channel in a multi-channel convolution has its own filter, what difference does it make if, ...
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18
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Monitoring validation loss with confident mispredictions
I am working on a binary classification task using a variant of ResNet. The dataset consists of medical recordings and is relatively small (N=2000), though I apply various validated data augmentation ...
- 101
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10
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Train a decoder convolutional-based network with a non-differentiable classifier
Suppose you have a decoder network $D(\textbf{x})$ which outputs a latent representation $\textbf{z}$. My idea is to give such latent representation to a generic non-differentiable classifier $h(\...
- 101
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2
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43
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Adding one new class of images to the dataset and re-training the classifier model
I have a dataset of 10 types of images. I trained AlexNet to classify these images, and it gives decent results (around 80% accuracy).
Now, I want to add the 11th class of images. Of course, I can ...
- 103
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26
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Why isn't my CNN-RNN model learning despite the CNN performing well?
I'm working on a model that combines a CNN with an LSTM to process sequences of spectrograms and make per-time-step predictions. The CNN alone performs well on the task, but after adding an LSTM for ...
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1
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46
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Where am I going wrong in my CNN approach to automate cropping images?
I have a dataset compiled of geological images. They often have unnecessary padding to the left, right, and bottom. I also have a folder containing cropped versions of these images where the padding ...
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1
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69
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Fine-tune vs training from scratch
I'm training a 2 class Yolov8 Small detection model, and iterated through the model re-training over the previous best model a few times. Now, I added more data and the size of dataset is 2x (200,000 ...
- 101
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36
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Why are video game upscaling models based on convolutional networks?
Why they use such networks, even through GANs and diffusion models can also be used for image upscaling? Examples of such models include Nvidia DLSS and AMD FidelityFX Super Resolution (FSR).
user86196
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9
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Neural Networks that fit vector transforms
I have a CNN that is image to image and maps a binary image input to a binary image output. These are usually simple shapes, like a rectangle or a circle. Usually they become smoothed a bit (the ...
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27
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Music simplifying modeling
I'm working on a machine learning task involving a unique dataset of piano tracks, represented as arrays with the shape (200, 10, 5000). Here, 200 is the number of tracks, 10 represents the number of ...
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21
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What is "Explicit Propagation" Image Inpainting - LatentPaint Paper - Generative AI
I am trying to implement "Explicit Propagation" method for Image Inpainting introduced in LatentPaint Research paper.
This paper proposes the introduction of a module between VAE and ...
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1
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29
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Deciphering terse CNN specifications
I'm reading a paper about three CNNs, described in a terse way that I cannot decipher (usually the case for space-efficient papers). Likely, this is due to my ignorance and I wonder if anyone can ...
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14
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Why VGG fine tuning process keeps crashing in colab?
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I am trying to fine-tune a VGG16 model for an ECG-image classification task with the resolution of 1700 * 2200
I haven't reshaped and reduced the size of the images because I thought some ...
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25
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How to implement differentiable sinusoidal basis functions for a convolutional FFT module in torch
I currently have made a convolutional network module in torch that makes the basis functions in the usual way fourier_basis = np.fft.fft(np.eye(frame_size)) and I ...
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15
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Sinusoidal Kernel Initializer + 1D-CNN Implementation
I am trying to replicate the MSK+CNN model in this research paper: https://ieeexplore.ieee.org/document/9760210/authors#authors But I am not sure how to implement it correctly. It is suggesting that ...
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42
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How exactly is the dynamical unfolding implemented in ByteNet?
I am thinking about making use of ByteNet (https://arxiv.org/abs/1610.10099) architecture for a project, and would like to get a better understanding of how the model works.
I've read through the ...
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2
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164
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How do multiple filters in a CNN work?
I understand how kernel size, stride, and the basics of CNNs. My question may look simple, but, despite my efforts, I haven't found the answer yet.
In the following figure, let a $224\times224$ image ...
- 113
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1
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122
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About the Matrix Multiplication in Fully Connected Neural Networks
I've learned some machine learning networks, and I think they're not that interesting. Sometimes I feel like they're just messing around with formulas and creating something that doesn't make sense. ...
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21
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Federated CNN largely predicts only one (wrong) class
I have trained a CNN for the CIFAR dataset using federated averaging, adhering to this tutorial: https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification. However, my ...
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24
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Why can't I replicate the validation loss from a Keras tuner (LSTM)
I feel like I'm doing a pretty straightforward sequence of tasks and must be making a simple mistake - I simply build a sequential model, tune it, build a clone of the optimal model by extracting the ...
- 101
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76
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What's the simplest deep learning approach in signal processing? A 1D CNN?
I have a three datasets each containing one signal of a specific type (normal, periodically jammed, constantly jammed). I would like to experiment with this dataset to predict wether a signal is ...
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24
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What's the advantage of multi-GPU training in Alex-Net?
I was reading ImageNet Classification with Deep Convolutional Neural Networks(Alex et al) and they trained their model on two GPUs following fine-grained structure. Can you tell me why they chose that ...
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13
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Self-supervised learning weights initialization "after" projection head
For most Self-supervised learning algorithms: SimCLR, MoCo, BYOL, SimSiam, SwAV, etc., its common to have a projection head after the base encoder (which in most cases is a vanilla ResNet-50 CNN). An ...
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15
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What is the reason for the difference between the expected input tensor order for LSTM and Conv1d?
What is the reason for the difference between the expected input tensor order for LSTM and Conv1d?
Say I have an input tensor for time series data of shape ...
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17
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Trying to understand backprop with convolution mathematically
I'm trying to understand how backpropagation works mathematically with convolutions.
I have a VERY simple setup.
X input to Y output through a linear filter:
kernel = torch.ones((1, 1, 3, 3)) / 9 # ...
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14
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Why time based neuron still needed when the time-series data can be converted to time-freq domain (image) and use CNN for that?
LSTM, BI-LSTM, GRU, RNN are time-step based neuron. Why it still needed specifically for time-series data?
I mean, we can just transform the time-series data into spectrogram and use CNN for that.
For ...
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1
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125
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Kernels on a trained CNN seem random
I saw this question and am having the same issue: kernels on a trained CNN look random.
I am using Pytorch to train a CNN, and based my code on examples that train the MNIST characters: ~50 ...
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2
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1
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64
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Distinguishing between the fundamental structures of the convolutional neural network and the recurrent neural network: hierarchical vs sequential
I'm trying to distinguish between the fundamental structures of the convolutional neural network and the recurrent neural network. Convolutional neural networks build a hierarchical model from the ...
- 603
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24
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Is it possible to have weightened (for every layer) gradient?
Imagine having pretrained network(resnet, ...) If we want to train for new classification task we change last layer and maybe freeze some first leayers. But what if we have another network that ...
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12
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Understanding matching of a CNN Layer's Output With the Receptive Field of Input Layer
I was trying to implement the following paper: https://arxiv.org/abs/1610.01563 and I came across something that seemed ambiguous to me. On page 4, second paragraph, it says
After processing the ...
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104
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How to solve the exploding gradient problem in VAE training?
I was trying to implement VAE on the CelebA dataset inspired by the Tensorflow implementation of MNIST. I have tried varying batch size but there seems to be no effect from that. The image formed is ...
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17
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How to get colored prediction output in multiclass UNET segmentation
i have the same data and annotation just like show in the provided link,
https://stackoverflow.com/questions/60019869/u-net-how-to-improve-accuracy-of-multiclass-segmentation/78453876#78453876
query : ...
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1
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58
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How to calculate the gradient for the output with respect to the input pixels
Hi for my project I'm using a somewhat simple CNN consisting of several convolution layers and pooling layers. Essentially the model is trained to perform a blur of sorts on an input image.
For my ...
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30
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CNN: Accuracy gap of 5-7 % between accuracy computed on-the-fly and separate model evaluation on the training set
I am training a CNN for some basic classification task. During training, I compute the training accuracy after every epoch. After the training has finished, I evaluate the model again on the entire ...
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67
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Visualization of Transposed Convolutions
After reading on Transposed Convolutions and Fully Convolutional Networks in the d2l book (14.10 and 14.11), I wondered about the visualization of transposed convolutions.
As you probably know, ...
- 153
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57
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I can’t pass a treshold no matter what I do
I am currently training an CNN for classification. My training data are 80x80 images, 3 channels, which I have grouped into 25% validation, 75% training, all evenly distributed. I have 3 classes into ...
- 1
0
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27
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Preparation of multivariate time series data
I am doing a university project on index/stock price prediction.
I plan to use a combined cnn-lstm model, and I have several different types of data: Open High Low Close Volume, values, fundamental ...
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