Questions tagged [semi-supervised-learning]
For questions related to the machine learning technique called semi-supervised learning, which is a combination of supervised and unsupervised learning.
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References for the theory of pretraining and unsupervised learning to improve subsequent supervised learning
I am not sure if the title of this post uses the correct terminology, so suggestions are welcome.
I have been following a lot of the ideas of using Pre-training methods on neural networks, to improve ...
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Very high ACC (ca. 95%) with 1DConvNet for Time Series
Does this sound legit, for people working with CNN and Time Series?
I have a Framework that applies Dynamic Tim Warping (DTW) on time series, using the DTW distance matrix, I cluster my data and ...
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Semi-supervised learning algorithms creating redundant data
If I'm generating pseudo-labels that I'm confident are correct for my dataset due to high confidence scores or something else, how can I expect that the new data I'm labeling won't be redundant? To my ...
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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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Is the initial teacher model in the Noisy Student algorithm noised?
Reading through the paper on the Noisy Student algorithm, I have a quick question about how the initial teacher model is built.
In step 1 of the algorithm, the loss function is defined such that it ...
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Image segmentation when given masking information is incomplete
In my problem, there are about 5,000 training images and there are about 50~100 objects of identical type (or class) on average, per image. And for each training images, there is a partial mask ...
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How are GCN doing semi-supervised learning?
In Semi-Supervised Classification with Graph Convolutional Networks, the authors say that GCN is an approach for semi-supervised learning (SSL).
But a GCN is making predictions using only the graph ...
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Validation set performance increasing even after seemingly overfit on training set
I am training a semi-supervised GAN network using data from multiple subjects. I separated the labeled and unlabeled data based on my subjects, so there is no leakage, while having much more unlabeled ...
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Why is it difficult to propagate intransitive relations over a graph?
In the paper Semi-Supervised Learning by Mixed Label Propagation, they say
One major limitation with most graph-based approaches is that they are
unable to explore dissimilarity or negative ...
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GAN Generator Output w/ Periodic Noise
I am training a Semi-Supervised GAN, using multivariate time-series with window of shape (180*80) with the generator and discriminator architecture below. My data is scaled using Robust Scaler, so I ...
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Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?
I want to train a convolutional neural network for object detection (say YOLO) to detect faces. Consider this image:
In this training image, I have many people, but only 2 of them are annotated. Is ...
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Model output segmentation maps which are not full
I created a VGG based U-Net in order to perform image segmentation task on yeast cells images obtained by a microscope.
There are a couple of problems with the data:
There is inhomogeneity in the ...
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Should forecasting with neural networks only be treated as a supervised learning (regression) problem?
I have recently made a work about the application of neural networks to time series forecasting, and I treated this as a supervised learning (regression) problem. I have come across the suggestion of ...
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How can the expectation-maximization improve the classification?
I am learning the expectation-maximization algorithm from the article Semi-Supervised Text Classification Using
EM. The algorithm is very interesting. However, the algorithm looks like doing a ...
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What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?
I am following the NLP course taught by Dan Jurafsky. In the video lectures Supervised Relation Extraction and Semi Supervised and Unsupervised Relation Extraction Jurafsky explains supervised, semi-...
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What's the intuition behind contrastive learning?
Recently, I have seen a surge of papers w.r.t contrastive learning (a subset of semi-supervised learning).
Can anyone give a detailed explanation of this approach with its advantages/disadvantages ...
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Semi-supervised: Can I predict the label of purposely unlabelled observations?
Let's say I have a data set with of length N. A small proportion N2 is labeled. Can I remove some labels and then 'reverse' this action with a trained neural network? I could then use the same process ...
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What is the difference between graph semi-supervised learning and normal semi-supervised learning?
Whenever I look for papers involving semi-supervised learning, I always find some that talk about graph semi-supervised learning (e.g. A Unified Framework for Data Poisoning Attack to Graph-based Semi-...
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What does "class-level discriminative feature representation" mean in the paper "Semi-Supervised Deep Learning with Memory"?
I am reading the paper Semi-Supervised Deep Learning with Memory (2018) by Yanbei Chen et al. The topic is the classification of images using semi-supervised learning. The authors use a term on page ...
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How do I locate a specific object in an image?
Some pictures contain an elephant, others don't. I know which of the pictures contain the elephant, but I don't know where it is or how does it look like.
How do I make a neural network which ...
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What is the relation between semi-supervised and self-supervised visual representation learning?
What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
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How to deal with a small amount of labeled samples?
I'm trying to develop skills to deal with very small amounts of labeled samples (250 labeled/20000 total, 200 features) by practicing on Kaggle "Don't Overfit" dataset (Traget_Practice have ...
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What is the definition of "soft label" and "hard label"?
In semi-supervised learning, there are hard labels and soft labels. Could someone tell me the meaning and definition of the two things?
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