All Questions
Tagged with convolutional-neural-networks deep-learning
296 questions
0
votes
0
answers
4
views
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,...
1
vote
1
answer
31
views
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 ...
- 11
0
votes
0
answers
19
views
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 ...
- 1
0
votes
0
answers
19
views
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 ...
0
votes
1
answer
70
views
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
0
votes
0
answers
21
views
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 ...
0
votes
0
answers
13
views
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 ...
- 235
0
votes
0
answers
15
views
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 ...
- 1
0
votes
0
answers
14
views
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 ...
0
votes
0
answers
27
views
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 ...
- 1
0
votes
0
answers
24
views
CNN model configuration: advice
Assume that a CNN model is to be developed to recognize commercial domestic planes flying in the sky. The training data should include images of flying domestic planes for true positives. Additionally,...
- 1
0
votes
1
answer
71
views
How to get Complexity per Layer, Sequential Operations and Maximum Path Length in CNN architecture?
In the paper Attention is all you need, here is Table 1, can someone explain what architecture is referred to in the "Convolution" row and hence describe the other 3 columns in it? The other ...
- 11
0
votes
0
answers
13
views
How to do object detection for 1 object in the image, with 3 possible classes (in a custom dataset)
I am new to deep learning, I hope you will lead me because I have been stuck for a week. I am trying to build a model for identifying a single object in the image.
So, I made my custom dataset, which ...
- 1
0
votes
0
answers
37
views
1D CNN with Single vs. Two Channels for Number Image Recognition
I am taking images of numbers as input, in a convolutional neural network and building a model to predict the number.
In particular, I am building a one dimensional convolutional neural network with ...
- 121
0
votes
1
answer
45
views
CNN multioutput regression architecture modification
I am working on a regression task where the model has to predict two values at the same time. The idea is that the dataset consists of 16 features, where the first 8 features represent the first value ...
0
votes
1
answer
47
views
CNN Input shape for DQN Q-calculating Network
Context: I want to build a DQN with as CNN for calculating its Q value on each step.
Enviroment's status can be described by the attributes of 3 machines (each one with own attributes). I'd also like ...
0
votes
0
answers
62
views
Trained model on cifar10 performs poorly on real images
So I'm trying to train a model using the CIFAR10 dataset.
The problem is that while the performance of the model on validation and test sets are good (about 95-96%), the model fails to predict images ...
1
vote
0
answers
86
views
How do transformer-based architectures generate contextual embeddings?
How do transformer-based architectures like Roberta generate contextual embeddings? The articles I've read keep saying that transformer encoders work bidirectionally. Because of self-attention, they ...
user77925
0
votes
0
answers
81
views
What are the differences between Inception Score and Fréchet Inception Distance?
From the articles I've read about image generation using GANs, the Inception Score measures two things simultaneously: the variety of images (diversity) and the distinct quality of each image. Does ...
user77925
5
votes
1
answer
794
views
How can the discriminator determine the sample is fake or real?
Based on the articles I've read, the discriminator can identify whether a sample is fake or real. However, the articles don't clarify the conditions used to determine if a sample is fake or real. I ...
user79662
2
votes
1
answer
152
views
What are meaning of parameters $\theta$ in this context?
I'm reading the article about generative model from Open AI, here is the section where they explain them:
Our network is a function with parameters $\theta$, and tweaking these parameters will tweak ...
user79662
1
vote
1
answer
639
views
Which epoch is the best for me to choose?
I have trained my deep learning model. I also saved the validation loss to a file and plotted on a graph
I have $2$ questions for this:
Does the validation loss look normal? Is there any issue with ...
user78437
1
vote
1
answer
115
views
Do GANs have constant running time?
After the model is trained, you just need to input random noise and the generator will output an image, does this mean GANs have constant running time ? I'm asking about both naïve GAN and variants of ...
user78202
1
vote
2
answers
259
views
The training process of a conditional GAN
For example, consider a dataset like MNIST. I give the conditional vector to produce only the number $7$ for both the generator and discriminator. In the following scenarios, what will the ...
user77925
0
votes
1
answer
386
views
In the conditional GAN (cGAN) architecture, why does the discriminator need conditional variable?
I'm reading about conditional GAN (cGAN) architecture, what I know is that the generator creates images combining both noise vector and conditional variable, the noise vector brings in random elements ...
user77925
1
vote
0
answers
89
views
Siamese network, cosine similarity unexpected result?
I was reading more about siamese network and it's use for similarity problems and I've stumbled upon this https://keras.io/examples/vision/siamese_network/
I was surprised to see both similarities in ...
- 11
3
votes
2
answers
200
views
How translation invariance is achieved in CNNs?
I am trying to understand how translation invariance is achieved in CNNs. For example, consider the following simple binary classification problem: predicting whether the letter that appears on an ...
- 231
1
vote
0
answers
38
views
Do all CNNs learn to detect edges in the first layer?
I was looking at 3D CNNs that process volumetric data, e.g. for MRI images of brain, where the input is a 4D tensor, and I couldn't find images from the filters of the first layer.
Suppose that ...
- 231
1
vote
2
answers
54
views
How do I assign a weight to an additional loss?
I am trying to do multi-spectral image fusion. I am using the following paper as a reference.
https://arxiv.org/pdf/1804.08361.pdf
The code available on GitHub works well. But, I am trying to add some ...
0
votes
2
answers
212
views
How vision models based on CNNs learn the relative positions of each pixel in the image?
A CNN model is based on a series of filters applied to an image. However, these filters can only "see" a small portion of the image and they have no information of the relative position of ...
- 63
0
votes
0
answers
35
views
CNN without actuators
After training CNNs without actuators, I have an idea to compare their weights with each other using image mirroring. I am looking for ideas about reality perception of CNNs in this way.
What might ...
- 1
1
vote
0
answers
20
views
Can I use zero-padded input and output layers in a 1D convnet to predict an element of interest from a variable-length input sequence?
I have developed a small encoding algorithm that accepts a time series of n = 750 samples and m = 1 feature from a scientific ...
- 111
1
vote
1
answer
44
views
How to deal with varying number of input images?
Im trying to use Deep-Learning to recognize breast cancer on Mammography Images. But in the dataset every patient has a different (1-4) number of images taken. How can i deal with that? Generally i ...
2
votes
2
answers
365
views
How are NNs output setup for games that allow multiple actions each turn and have very large sets of possible actions?
I was looking at an AI coding challenge for a two player game on a 2D grid of variable size (from one game to the next).
Here is a screen shot example of the playfield.
Each player has multiple units ...
- 121
0
votes
1
answer
56
views
Multi label classifier for patch wise predictions
If I train a multi label classifier on full images and then I feed some patches of these images will it accurately generate the labels which comes in that patch?
For example if I train an image ...
- 3
2
votes
0
answers
31
views
How can I learn about NN architecture?
I have a pretty good understanding of individual neural net layers (fully connected, convolution, pooling, activation, etc) but struggle to construct combinations of them to solve a given problem. I ...
- 21
0
votes
0
answers
41
views
How can validation accuracy be more than test accuracy?
I have been trying to implement DenseNet on small dataset using k-fold cross validation. Training accuracy is 94% ,validation accuracy is 73% whereas test accuracy is 90%.I have taken 10% of my total ...
- 13
2
votes
1
answer
143
views
Is transfer learning effective when the new task has more classes than the original?
All examples of transfer learning I have seen for classification use initial weights of a network trained on a larger number of classes (say 1000 in the case of networks trained on ImageNet data) to ...
0
votes
3
answers
1k
views
why validation accuracy be greater than training accuracy for deep learning models? [closed]
I hope you are well.
I had a problem and didn't understand the answers given on questions similar to my question.
If possible, please answer this problem in a simpler way.
Val_acc : %99.4 _
Train_acc :...
0
votes
3
answers
237
views
How to encode categorical data for a convolutional model?
Is there a way to encode categorical nominal (no ordered) data to be used in CNN models?
Let's say I need to create a 1D CNN model for categorization of time series but the values are not measurements,...
- 1,280
1
vote
1
answer
89
views
Why training the same model on the same data can be slower on better card?
Can someone explain why training CNN model (in my case DenseNet201) on the same data, and the same data processing pipeline can be slower on better GPU (RTX3090) than worse one (RTX3060), with the ...
- 1,280
0
votes
1
answer
170
views
Is data augmentation beneficial even if the dataset is large/diverse enough?
I'm training a deep learning model to map binary images to grayscale values of the same shape. For the dataset, I can genearate one as large and diverse as I want it to be.
My question is: let's say ...
- 101
1
vote
1
answer
753
views
How is a filter actually applied to all input channels in a ConvLayer2D
I was studying Convolutional Layers and some of their variations and I came across this post which says:
'For rgb vs greyscale, think about channels as feature maps for input layer and a filter gets ...
- 37
0
votes
1
answer
47
views
How to use strong labels in image classification?
I have a dataset where I have the labels cancer & non-cancer, and I also have localized pixel-level annotation masks of important regions/features in the images.
In a binary classification task, ...
- 11
1
vote
0
answers
39
views
How does a CNN work in detecting absence of features?
I'm trying to understand how a CNN operates internally. Let's say I'm doing binary classification with 1 output neuron and a sigmoid to classify dog vs no dog. No dog meaning the image does not ...
- 11
1
vote
2
answers
54
views
Dealing with incomplete file sets for a CNN for medical imaging regression problem
I'm trying to solve a medical imaging regression problem using a CNN. Each of the samples in my data set consists of one, two, or three of the following file types:
flair.nii.gz
mprage.nii.gz
swi....
- 125
1
vote
0
answers
47
views
Help with model architecture for a racing game
I’m working on a model for a racing game using pytorch. The model gets frame from the game as input and produces a controller state as output. The dataset consists of frames from the game and ...
- 11
1
vote
1
answer
76
views
Is it common that an applied deep learning research paper does not disclose any raw data and source code? [duplicate]
I think it is important for a research paper to include raw data and code for scientific replicability, verifiability, and falsifiability. However, recently, most of the research paper I read does not ...
- 163
4
votes
2
answers
60
views
Is there any proper literature on the types of features that different layers of a deep neural network learn?
Let's consider a deep convolutional network. It seems that there is some consensus on the following notions:
1. Shallow layers tend to recognise more low-level features such as edges and curves.
2. ...
- 141
2
votes
1
answer
694
views
Using GraphSAGE model for multigraph datasets
I checked out applications of GraphSAGE and it seems like its primarily used for single graph datasets. For example - Cora dataset - Its one big graph with 2708 nodes and 5429 edges. The model can ...
- 123