Questions tagged [unsupervised-learning]

For questions about AI that learns without being provided with a set of labels (expected answers) along with the set of input examples.

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Linear dependency between features on unsupervised learning

I am preparing a numerical dataset to be trained using unsupervised learning methods (i.e. association with Apriori algorithm) in order to try to find instrinsic correlations hidden in the data. ...
FELIPE_RIBAS's user avatar
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How to knowing number of clusters when using SOM?

SOM uses neural network. The output layer of SOM should be neurons position. As the model is training, neuron's position started to moving to the closer of centroid of clusters. The output layer was ...
Muhammad Ikhwan Perwira's user avatar
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Why are various deep learning models unsupervised? [duplicate]

While studying the field of deep learning, the questions I had from the beginning have still not been resolved. In general, supervised learning is known to solve problems by comparing the results ...
장민규's user avatar
1 vote
2 answers
91 views

What technique is used for training Large Language Models like GPT?

I'm learning about GenAI, such as GPT (Generative Pretrained Transformer), and I'm particularly interested in understanding the training techniques used for these models. Deep learning, generally, can ...
Exploring's user avatar
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Which main steps should I consider in order to successfully use a VAE for Anomaly Detection?

I am thinking about using the variational autoencoder model for anomaly detection . I have an Android Logs dataset. As the logs generated are a representative of time series type of data I thought ...
MLenthusiast's user avatar
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65 views

How can I combine unsupervised learning with supervised learning?

I am currently using an isolation forest (from sklearn library) to detect anomalies in a data frame (basically it's a dynamic data frame more of a kind of time series I am. But I have certain criteria ...
SUNITA GUPTA's user avatar
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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 ...
BKP's user avatar
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1 answer
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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 ...
Deadbeef Development's user avatar
1 vote
1 answer
146 views

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 ...
Muhammad Ikhwan Perwira's user avatar
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1 answer
22 views

Can we generate labels for an unlabelled dataset by doing some feature engineering?

I am very new to ML and currently, I am working on building a model that can predict recurring blood donors (a classification problem). I have a dataset which consists of 25 features (gender, height, ...
stkmnd's user avatar
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Can pretraining be continued after RLHF?

Assume you have a pretrained transformer language model (M1) which already underwent reinforcement learning by human feedback (M2). I assume that it is in principle possible to continue the ...
Hans-Peter Stricker's user avatar
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Is it possible to combine SGD with an unsupervised learning approach effectively

Before I undertake quite a large project I would like to clarify whether my idea for training a multi-layer neural network will work. I plan to make an AI that can land a rocket from randomly ...
Gamaray's user avatar
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36 views

Is regression method the best for my case?

newbie here. I'm starting to work on a custom model for a very specific task, so I found no pre-trained models for this task so far. After checking (un)supervised learning approaches I believe that ...
Putnik's user avatar
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How to compare word segmentation methods?

I am comparing a few methods of word segmentation in artificial language without dictionary and "golden" segmentation. Let's say, idolikecats is splitted ...
dobrowol's user avatar
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53 views

What is the difference between "Image Clustering" and "Unsupervised Classification" tasks?

I am trying to compare some results that I obtained in benchmarks with my unsupervised model. My model basically takes an unlabelled dataset and clusters it into semantic classes (10 clusters in the ...
puradrogasincortar's user avatar
14 votes
4 answers
8k views

What is the difference between self-supervised and unsupervised learning?

What is the difference between self-supervised and unsupervised learning? The terms logically overlap (and maybe self-supervised learning is a subset of unsupervised learning?), but I cannot pinpoint ...
Robin van Hoorn's user avatar
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1 answer
131 views

Can I implement a sklearn model inside a Pytorch nn.Module? [closed]

I am making a custom Pytorch model that at some point, clusters a latent space that was created by another, previous routine of the model (Autoencoder). In a bit more detail, my model is a regular ...
puradrogasincortar's user avatar
1 vote
3 answers
92 views

Do Artificial Neural Network with non-linear activation only in the output layer follows linearity?

I am using a model with linear activation in the hidden layer and non-linear activation in the output layer. Could you please help to know whether such models exhibit linearity or not? The non-linear ...
Prabal Devkota's user avatar
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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 ...
fkybrd's user avatar
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2 answers
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What can unsupervised learning actually be used for and how can humans interpret the outputs?

I am trying to refine my knowledge of AI, but unsupervised learning is a bit of a stumbling block for me. I understand that it finds 'hidden' patterns in data, but if they are hidden, how does a user ...
Robert Flook's user avatar
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1 answer
116 views

Unsupervised pretraining on the supervised learning training data

Is it ok to pre-train and train (fine-tune) the neural network on the same training data? Here is the specific context: I am using the TabNet model on a tabular dataset. The dataset is fully labeled. ...
Alireza Amani's user avatar
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Surveys, Papers, Hand on Tutorials about training data generation for anomaly detection

I am searching for anything related to supervised, semi supervised or unsupervised anomaly detection w.r.t training data generation. I am looking toward reading any work that tackles the issue how to ...
Skobo Do's user avatar
1 vote
1 answer
71 views

Deep Clustering Approach for Unsupervised Video Anomaly Detection

I'm working on Unsupervised Video Anomaly Detection, and I've tried implementing the Generative Cooperative Learning method, with the help of this paper. The method uses a fixed backbone (ResNext-101) ...
satan 29's user avatar
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1 answer
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Learning curve converges with huge errors

I am training an auto-encoder over $10^4$ epochs. I get a converging learning curve. However the error at the last stages stays huge $\sim10^{15}$. What does this mean? does it mean that my auto-...
devCharaf's user avatar
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1 answer
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Survey on non-machine learning object detection algorithms

I am working on a project in which I will be performing object detection on deformed objects. Unfortunately, there isn't enough data sets to train them on some neural network. I am looking for ...
UserX's user avatar
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Autoencoder make spectrogram important parts more pronounced with a "log loss"

Hi I want to create a neural network that essentially picks out the most pronounced parts of a spectrogram. Assume this is the True spectrogram: ...
GILO's user avatar
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1 answer
383 views

I have a 3 class classification problem. Detection of one of classes is very important. How to design the problem? one class classification or ...? [closed]

I have a 3 class classification problem. Correct detection of one of the classes is very important. How to design the problem: one class classification? a normal 3 class classification? two distinct ...
Hassan Zohrevand's user avatar
2 votes
1 answer
58 views

What clustering algorithms work best for datasets with only binary categorical features?

I have a dataset with a lot of binary categorical features and a single continuous target value. I would like to cluster them, but I am not quite sure what to use. In the past, I have used DBSCAN for ...
user199590's user avatar
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19 views

Is there an unsupervised learning method for determine the most common questions within a dataset?

I have a dataset consisting of questions from customers. I am curious of the n most frequent asked questions, regardless of the variation the questions might appear in. Is there NLP methods for ...
Adrian Evensen's user avatar
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1 answer
58 views

Is there a term for unquantifiably uncertain prior knowledge?

I'm working on a clustering algorithm which assigns each data point an index encoding its cluster. Index permutation is irrelevant to the correctness of the result. The algorithm is self-learning, in ...
programonkey's user avatar
2 votes
1 answer
739 views

Should I use an unsupervised approach or train a classifier with many classes to build a deep image feature extractor?

I'd like to build a deep feature extractor of images (using a Bi-linear CNN). What would lead to the best results: an unsupervised approach (such as https://iopscience.iop.org/article/10.1088/1742-...
Adrien Nivaggioli's user avatar
1 vote
1 answer
104 views

What happens if all the features are correlated with each other before clustering?

I know that when two features are highly correlated with each other, one of them should be removed from the dataset so they don't add twice the weight. However, what if all my features share a ...
Sanzor's user avatar
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2 votes
0 answers
27 views

What are the benefits of using spectral k-means over simple k-means?

I have understood why k-means can get stuck in local minima. Now, I am curious to know how the spectral k-means helps to avoid this local minima problem. According to this paper A tutorial on Spectral,...
Amartya's user avatar
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0 answers
26 views

How to group multi-dimensional audio, video, and numerical data based on relatedness?

I have a data set that includes image arrays, point clouds, audio waveforms, and plain numerical data. I want to use unsupervised learning to group the data based on relatedness. So, if the audio and ...
DragonflyRobotics's user avatar
1 vote
1 answer
674 views

Test accuracy decreases during my train process

I want to train a neural network model with the arcface loss function and try to combine it with domain adaption. But when the training process continues, I find the test accuracy first increases and ...
klayoe's user avatar
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0 votes
1 answer
52 views

Generating a dataset from data with "assumed" lables

I've got a task similar to the following: Out of x amount of people, I need to predict, who could be a good athlete and who not. The thing is, I don't have data on the athletic performance of those ...
Chris's user avatar
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0 votes
1 answer
119 views

In this example of fuzzy c-means, what is the difference between "sigma" and "center" for the clusters?

In this example, what exactly do "Cluster" and "Sigma" mean? (They chose random coordinates for the three centroids of the groups) Centers: Cluster centers, returned as a ...
user5520049's user avatar
-1 votes
1 answer
42 views

What is the borderline between unsupervised learning and regular algorithms?

Unsupervised learning using neural networks is clearly machine learning since it is utilising neural nets. However, some algorithms, k-means clustering, for example, are considered unsupervised ...
Dan D.'s user avatar
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0 votes
1 answer
224 views

Is there a way to select the subset of most important features using PCA?

Is there a way to select the most important features using PCA? I am not looking for the principal components with the highest scores but a subset of the original features.
Mika's user avatar
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0 answers
64 views

Loss function to Push response value towards extremes

I have a feature map whose values are in the range of [0,1]. I want to push these values either towards extreme 0 or 1 using some loss function. Since I don't have any target value so it had to be in ...
user3585510's user avatar
0 votes
1 answer
66 views

How does CURL extract labels from logits? [closed]

While going over the pseudocode of the CURL paper, the method to identify labels from the logits wasn't clear to me. I believe this technique might be common in other PyTorch/Deep Learning tasks. I ...
desert_ranger's user avatar
1 vote
0 answers
35 views

How to learn transition type in a 1-hour extended DJ Mix?

How would you design a model which learns the transitions in a given 1-hour DJ Mix? To be specific, the model should be able to learn transitions, specify the occurring time and the type (Crossfade, ...
Cactuser's user avatar
1 vote
0 answers
21 views

What is the best clustering method to detect anomalies for data with mostly categorical data?

I have a dataset with about 85 columns. Out of the 85 columns, 70+ are categorical. My goal is to identify the outliers in this dataset through clustering methods as I do not have a target column. ...
user13074756's user avatar
1 vote
0 answers
21 views

Can unsupervised models learn something from cat vocalizations?

I love cats, and over the years have noticed that they have recurrent patterns of vocalizations. For example, upon seeing a bird, a cat may start chittering, but the same cat would never chitter at ...
Alex Stone's user avatar
2 votes
0 answers
172 views

Does Yann LeCun consider k-means self-supervised learning?

I was discussing the topic of self-supervised learning with a colleague. After a while we realized we were using different definitions. That's never helpful. Both of us were introduced to self-...
Pieter's user avatar
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2 votes
1 answer
97 views

Is there a clustering algorithm that can make n clusters and the n+1 "others" cluster?

As far as I know all clustering algorithms assume that all delivered data points have to find its cluster. My question is, is there an algorithm that could focus only on n clusters (number stated by ...
GKozinski's user avatar
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4 votes
1 answer
193 views

What is the relation between self-taught learning and transfer learning?

I am new to transfer learning and I start by reading A Survey on Transfer Learning, and it stated the following: according to different situations of labeled and unlabeled data in the source domain, ...
Kais Hasan's user avatar
3 votes
1 answer
7k views

Is there a bias-variance equivalent in unsupervised learning?

In supervised learning, bias, variance are pretty easy to calculate with labeled data. I was wondering if there's something equivalent in unsupervised learning, or like a way to estimate such things? ...
user avatar
0 votes
1 answer
91 views

Which algorithm can be used for extracting text patterns in tabular data?

I am working with tabular data that is similar to the below: Name Phone Number ISO3 Country Amount Email ... ... Outcome Possible Reason Leona Sunfurry (555)-555-5555 United States 58.96 leo_sun@...
Petar Luketina's user avatar
4 votes
2 answers
84 views

Which metric should I use to assess the quality of the clusters?

I have a model that outputs a latent N-dimensional embedding for all data points, trained in a way that clusters data-points from the same class together, while being separated from other clusters ...
jaeger6's user avatar
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