Questions tagged [deep-learning]
For questions related to deep learning, which refers to a subset of machine learning methods based on artificial neural networks (ANNs) with multiple hidden layers. The adjective deep thus refers to the number of layers of the ANNs. The expression deep learning was apparently introduced (although not in the context of machine learning or ANNs) in 1986 by Rina Dechter in the paper "Learning while searching in constraint-satisfaction-problems".
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Prefix tuning in LLM uses learnable vectors to fine tune the model
I would like to implement a new architecture for Transformer.
Below description is my thought.
Prefix tuning in LLM uses learnable vectors to fine tune the model.
Is there a way to use the output ...
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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 ...
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What are the advantages of GANs over Diffusion Models in image generation?
Diffusion Models have recently gained popularity in the field of image generation, with widely used products such as Stable Diffusion employing this approach and yielding impressive results. While ...
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Question about the Conditioning Augmentation technique?
In the paper StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, the goal is to convert text descriptions into images. The text encoder encodes the ...
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How to detect negative (absence of) an object?
I want to detect the people that are NOT wearing PPE vests using a pre-trained object detection model like YOLO or Grounding Dino.
The models are able to detect people and vests separately, but I am ...
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how to determine the number of units for dense layer for transfer learning?
I'm using MobileNetV2 for classification, and I want to add dense layers(i remove the last layer of the MobileNetV2 model). How do I choose the number of units for the dense layer after obtaining the ...
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Can't get a correct accuracy on tabular data using deep learning
This is my first message here, and I would like to seek some assistance !
I have a technical test for a job that I really want, and I have 10 days to complete it. I've attempted to work on it, but I'm ...
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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 ...
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Diffusion model for image to image translation
I am interested in using the diffusion model for image-to-image translation (pair images). I used this repository for semantic image synthesis via diffusion models Code. How can I use it for image-to-...
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Can mini-batches for stochastic gradient be balanced but not representative of the training data?
When we construct mini-batches for stochastic gradient, it is important to ensure that the different mini-batches are balanced (for example, in case of classification they contain the same ratio for ...
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My model is only improving when learning rate is 1. Should I be worried?
As the title says my GNN with three layers of GAT (Graph attention layers) is only moving the metrics when the learning rate is 1. As generally the learning rate is (0,1) should I be worried?
Also ...
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the best choice to reduce a features vector
i have 1200 features highly correlated , and i want to reduce those number of features
so the best choice is use feature selection or dimensionality reduction? and which method is the best in this ...
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Are foundation models something fundamentally new? Is there a proper definition?
Currently, one can hear more and more about "foundation models" but details of this are not always clear. Also, I even have the impression, that sometimes people don't talk about exactly ...
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What is the potential issue of nested neural networks
everyone. I am working on a nested neural network architecture. For the sake of better understanding my question, simply assume the loss is
$L = G(k’) - H(k'')$
where $G$ and $H$ are two functions we ...
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Keeping the grad of the tensor when using inverse_transform
To train a network, I scaled both the input and outputs of my data like the following:
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Why my deep learning model (FCNN/ 1DCNN) fails to learn when training on medical dataset?
I am working on a project to predict the severity of the disease, Hemophilia using a deep learning model(FCNN or 1DCNN). I am working based on the information provided in this article: https://www....
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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 ...
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Regression loss conditioned by the ground-truth values
I'm working on a regression problem with a CNN in which the input is a single image, and the output is an angle in degrees (which determines a specific measure related to the image).
Sometimes, the ...
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Book suggestion about deploying real deep learning models in real world
Can you suggest books about deploying machine learning algorithms on robots, especially on real time stream? I don't know how to deal with latency and other challenges that real time inference/stream ...
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Encoding categorical data with "many" unique values in neural network
I am new to machine learning, in fact, I am implementing my first deep neural network from scratch without any framework.
The dataset has 3500 rows, and 4 categorial columns of which two have about ...
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how to know to define asuitable posterior for bayesian deep learning
I work with model its purpose for classification task in medical image, i want also to estimate uncertainty , so i work with Bayesian technique , i use a pre-trained model for feature extraction and ...
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I'm trying to build image search like Google Photo-Image with face is given to model & it'll get all the images in database in which he/she is present
When a user upload a selfie, the model search same person in dataset of images of multiple persons and get back all the images in which that person is present.
Step 1: From dataset of images I detect ...
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Classes definition for detecting impervious surfaces on aerial photographies
My project is to use deep learning, essentially a UNET segmentation model, to detect impervious surfaces on high resolution aerial photographies.
I wonder if it's better to train the model with many ...
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Inquiry on Combining Two Neural Networks for unsupervised training: Has This Been Researched?
Hello AI Stack Exchange Community,
I am exploring an idea related to neural networks, and I'm curious to know if this method has been previously researched or if there is a specific term for it.
I am ...
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Has There Been Research on Using a Neural Network as a Loss Function for Another Neural Network?
I'm intrigued by the idea of employing a separate neural network (which I'll refer to as the "loss network") to compute the loss for a primary network based on its inputs and outputs. The ...
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Why is a linear and not a non-linear transformation used in self-attention to calculate queries, keys and values?
In self-attention, one vector undergoes three different transformations to create the query, keys and values. These are always a simple linear transformations. Why is it considered sufficient? Shouldn'...
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Deep learning to identify and classify first and last names into gender, age range and probable region of origin [closed]
I have a bit of a question as to how I could architect a pipeline of deep learning for the following use case:
gender identification from a full name (joseph is male, josephine is female, sasha is ...
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Watermark removal without inpainting
Suppose I have a bunch of images that have been watermarked with a transparent logo with some unknown blending function g as ...
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Are there neural networks that compute weights dynamically based on geometric attributes of neurons?
I am interested in exploring neural network architectures where the weights are not stored but are computed dynamically based on certain attributes or "dimensions" of the connected neurons.
...
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I have n-dimensional latent representational data, with a y logit label: How do I find peaks in the data using the label?
I essentially need a "find peaks" algorithm for when the input data is n-dimensional.
Specifically, in the latent space of my neural network I have have collected all the training data ...
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What algorithms could I use if I want to increase the accuracy of matched keypoints in an image pair?
Let's say that I used a keypoint detector like SIFT or SuperPoint to detect keypoints in image 1 and 2. Afterwards, I used a keypoint matcher to match corresponding keypoints in this image pair. The ...
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In the figure of Stable Diffusion, when does the switch part change?
In the illustration of Stable Diffusion, there is a concatenation part through Cross-Attention. Why is there a switch in the concatenation part?
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Multiple Loss Functions For Proper Parameter Updates [closed]
I am working on a model on PyTorch where it has two loss functions each fed from two separate input datasets. I want to update the model parameters based on these loss functions, ic_loss, res_loss, ...
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Building an algorithm that detects an area delimited by a quadrat
I want to build an algorithm that takes an image and outputs the same image, but cropped so that it focuses only on a particular area delimited by a quadrat, like so:
This is not my image as I am not ...
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Data out of scale
Imagine that you want to train a deep learning model for forecasting/transforming data and have a training dataset with endpoints 0 and 1000. Now, Imagine receiving a new dataset with endpoints 0 and ...
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Exploring the Similarity of Sibling’s Voices Using Automatic Speaker Recognition
I want to start project on Exploring the Similarity of Sibling’s Voices Using Automatic Speaker Recognition
Everyone has a unique voice, because of the different structure of their articulatory ...
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Possible Reasons for the Discrepancy in Trainable Parameters of the Extended DeepConvLSTM Model
I have implemented DeepConvLSTM baseline Model input are 60×d frames each representing 60 samples with d features. Frames are fed into four consecutive convolution layers with standard rectified ...
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Reinforcement Learning vs Supervised Learning [duplicate]
I have never tried reinforcement learning in my life.
I'm planning to apply it in robotics.
I have some experiences using supervised learning mainly deep learning. So, that's mean I will use neural ...
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Choosing a Deep Learning model to analyse microscope images
I have lots of simulated training data of microscope images and I want to train a network to count the number of points in the image. These points are distributed in concentric oblate shells. The ...
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Is it possible to have a good validation accuracy with random labels?
I'm currently trying to train a siamese network to determine if two inputs are similar or not. The inputs are power consumption traces and I'm basically using the siamese network as some sort of ...
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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 ...
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How to force Transformer to give more weight to certain tokens
I'm developing an encoder-decoder based transformer model and I would like to ask if there are ways to incentivize or penalize certain tokens during training.
I'm working on a translation task where ...
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How does a zero-order hold kernel in a Convolutional Neural Network look like?
Several papers co-authored by Hitoshi Kiya propose to use a fixed convolutional layer with a zero-order hold kernel to avoid checkerboard artifacts in CNNs. [1, 2, 3]
While there is plenty of ...
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LSTM with multiple data streams
I am working on the following problem:
I have ~10 weather stations in somewhat approximate areas, at some points during the day (different for each station), I get readings of various data points (...
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Why does Stable Diffusion use VAE instead of AE?
I am currently studying the Latent Diffusion Models (LDMs) and am interested in training my own model using a unique dataset. In my research, I came across Stable Diffusion (SD). Some sources suggest ...
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Which process is better to understand images?
What is the difference between this process of recognizing objects in a image:
(The correlation function calculate the correlation coefficient between the input and a image containing the object we ...
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RAM usage increases linearly while training. What could be causing the issue?
I'm training a siamese neural network with semi-hard batching, RAM usage increases linearly over epochs, peaking at 60GB, then switches from GPU to CPU, slowing down training. I'm using Windows with ...
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Gradient: any resource on how to understand everything about it?
I have read some resources about AI, and they all speak about the gradient.
Is there any book focused on this? maybe with tons of images / diagrams?
Cheers
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How to use deep learning to train an AI to play a two-player tactic game like rock-papers-scissors?
I want to try to train an AI model that can play a simple two-player tactic game. The game is not rock-papers-scissors, but have similar properties.
Firstly, two players present their moves ...
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autoencoders for anomaly detection, training individual models for different users or roles, how?
Do I first train a generic model for all of my users on a network, say for a network anomaly detection example, then fine tune for each user on their own subset of the training data? But I'd be using ...
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