Skip to main content
Share Your Experience: Take the 2024 Developer Survey

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.

Filter by
Sorted by
Tagged with
0 votes
0 answers
11 views

Semi-supervised learning - focus on incorrectly predicted examples

For sake of simplicity I have a series of images and I'm trying to detect if it is of a cat. I've got a small set of completely labelled images - half of them containing a cat, and half not - and a ...
codythecoder's user avatar
0 votes
1 answer
27 views

Image segmentation with noisy labels

I have a dataset which consists of satellite images and their labels which are indicated by let say class 1 and 2. I want to perform image segmentation to detect pixels related to class 1 and 2. The ...
Carol's user avatar
  • 1
0 votes
0 answers
45 views

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 ...
krishnab's user avatar
  • 207
0 votes
0 answers
19 views

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 ...
Skobo Do's user avatar
0 votes
1 answer
40 views

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 ...
sangstar's user avatar
  • 131
0 votes
0 answers
7 views

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
60 views

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 ...
lamyvista's user avatar
1 vote
2 answers
968 views

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 ...
jeff pentagon's user avatar
1 vote
1 answer
1k views

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 ...
willtryagain's user avatar
0 votes
2 answers
164 views

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 ...
Hazar's user avatar
  • 31
1 vote
0 answers
24 views

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 ...
willtryagain's user avatar
4 votes
1 answer
303 views

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 ...
Hazar's user avatar
  • 31
0 votes
1 answer
97 views

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 ...
S.E.K.'s user avatar
  • 71
1 vote
0 answers
22 views

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 ...
David's user avatar
  • 188
2 votes
1 answer
89 views

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 ...
David Díaz's user avatar
0 votes
0 answers
73 views

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 ...
Cheleeger Ken's user avatar
0 votes
1 answer
620 views

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-...
DRV's user avatar
  • 1,693
2 votes
1 answer
715 views

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 ...
CATALUNA84's user avatar
0 votes
0 answers
37 views

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 ...
Dirk N's user avatar
  • 158
2 votes
1 answer
111 views

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-...
boomselector's user avatar
2 votes
1 answer
251 views

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 ...
IntegrateThis's user avatar
2 votes
1 answer
172 views

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 ...
user31264's user avatar
  • 203
13 votes
3 answers
13k views

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's user avatar
  • 281
10 votes
1 answer
376 views

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 ...
FirePower's user avatar
  • 201
12 votes
1 answer
18k views

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's user avatar
  • 121