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.
136 questions
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What is the problem called if I only label few data, the rest data is unlabeled and then train them?
Suppose of MNIST data, if I only label once for every possible digit (10 digits) and leave the rest unlabeled. Then I train them with multi-task learning, where the first task is classification (only ...
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Extract intents from a conversations/transcripts data dump in unsupervised mode
I want to develop a tool that will take a bunch of conversation texts from call centers (between agents and users) and be able to build a multi-intent hierarchical structure based on the input data ...
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How to Apply a Pre-trained Jigsaw Puzzle Model for Transfer Learning on Larger Images?
I’ve read the article titled Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles.
In this article, the authors create jigsaw puzzles and train a model to solve them. The process ...
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Recommend unsupervised ML models
for my own interest, I am interested to know if there are any recommended unsupervised models (maybe ML) that is capable of doing the following. I would like to try it in the exact way (no ...
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Is there any unsupervised online learning rule for classical neural networks?
In spike-based neural networks, there is a learning rule called STDP (Spike-Timing-Dependent Plasticity).
It's a completely unsupervised learning rule that works continuously when data is fed into the ...
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Troubles using unsupervised domain adaptation
Hope somebody can help me, I've been stucked on this and there's no way I can find the origin of my problem...
So I have a model that I have fine-tuned, it's a resent18 that looks like this (I'm just ...
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Is deep learning suitable/preferable for string similarity detection and application automation? If so, which type?
newbie here. I have developed an app that basically does:
Perform OCR, check if words are contained in the resulting text and then perform an action.
If no words are detected from the given list, ...
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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. ...
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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 ...
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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 ...
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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 ...
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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 ...
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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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486
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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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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, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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. ...
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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 ...
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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) ...
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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-...
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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 ...
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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:
...
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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 ...
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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 ...
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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 ...
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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 ...
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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-...
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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 ...
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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,...
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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 ...
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765
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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 ...
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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 ...
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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 ...
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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 ...
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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.
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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 ...
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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 ...
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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, ...
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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.
...
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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 ...
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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-...
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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 ...
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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, ...
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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?
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