Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

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.

See e.g. https://en.wikipedia.org/wiki/Semi-supervised_learning.

24 questions
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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?
0x90
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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?
ellie
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10
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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 provided all 20,000 Targets). I've read a ton of papers and…
FirePower
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4
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1 answer

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 kept linear activation for the generator…
Hazar
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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 treating this problem as an unsupervised,…
David Díaz
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2
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1 answer

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 and what are the cases in which it gives better…
CATALUNA84
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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-supervised Learning). What is the difference…
boomselector
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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 2 in the middle of the page that I am not…
IntegrateThis
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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 locates the elephant on a picture if it contains one?…
user31264
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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 looks like the initial teacher model is trained…
lamyvista
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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 information that denotes the polygon vertices of…
jeff pentagon
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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 Laplacian. The single place where I find the labels…
willtryagain
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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 similarity. This is because the dissimilar relation is not…
willtryagain
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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 amount of yeast in the images. 1 image can have…
David
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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 the accurancy of those networks on subsequent…
krishnab
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