11
votes
What is the definition of "soft label" and "hard label"?
According to Galstyan and Cohen (2007), a hard label is a label assigned to a member of a class where membership is binary: either the element in question is a member of the class (has the label), or ...
- 5,362
10
votes
What is the relation between semi-supervised and self-supervised visual representation learning?
Both semi-supervised and self-supervised methods are similar in the sense that the goal is to learn with fewer labels per class. The way both formulate this is quite different:
Self-Supervised ...
- 222
8
votes
What is the relation between semi-supervised and self-supervised visual representation learning?
Semi-supervised learning
Semi-supervised learning is the collection of machine learning techniques where there are two datasets: a labelled one and an unlabelled one.
There are two main problems that ...
- 39.5k
5
votes
What is the relation between semi-supervised and self-supervised visual representation learning?
The previous answer has given a good insight into the difference between two areas. I would like to give more examples.
Semi-Supervised Learning work with improving the data set by adding up new ...
4
votes
GAN Generator Output w/ Periodic Noise
Sorry cannot directly reply to your comment as I posted without an account, and you were right! I replaced transposed layers with Upscale1D+Conv1D and that solved the issue.
...
- 31
3
votes
Accepted
How are GCN doing semi-supervised learning?
In the introduction, the authors write
We consider the problem of classifying nodes (such as documents) in a graph (such as a citation network), where labels are only available for a small subset of ...
- 39.5k
1
vote
Image segmentation when given masking information is incomplete
If you have up-to 100 objects in a single image, their size must be fairly small percentage-wise. let's say their diameter is 50 pixels and the whole image is 1024 x 1024. Are the objects always non-...
- 737
1
vote
Image segmentation when given masking information is incomplete
If you are trying to make instance segmentation model, if you are trying to get full masking objects in your image then you are going into wrong direction.
Generally, when you train the model with ...
- 11
1
vote
Validation set performance increasing even after seemingly overfit on training set
If you ruled out leakage completely read this observation about double decent https://openai.com/blog/deep-double-descent/
This Blogpost from openAI shares the observation that the validation loss can ...
- 178
1
vote
Validation set performance increasing even after seemingly overfit on training set
You did a great job at this...
You can use the Tensorflow’s LogSumExp built-in function to avoid numerical problems. This routine prevents over/under flow issues that may occur when LogSumExp ...
1
vote
Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?
The neural network will learn what we teach it, for example with that image only, after finish training, your model will hard to recognize humans with dark skin, glasses, big eyes, etc, the features ...
- 902
1
vote
Accepted
Should forecasting with neural networks only be treated as a supervised learning (regression) problem?
I think the choice of technique strongly depends on how fine-grained your forecast-predictions need to be.
When it comes to forecasting by Reinforcement Learning (RL), one prominent example is the ...
- 815
1
vote
What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?
Supervised
Pros:
highest accuracy
Cons:
need a large human-labeled training set
brittle (doesn't work well with examples that are in a different genre from the training set)
Semi-supervised
...
- 111
1
vote
What's the intuition behind contrastive learning?
Contrastive learning is a framework that learns similar/dissimilar representations from data that are organized into similar/dissimilar pairs. This can be formulated as a dictionary look-up problem.
...
- 181
1
vote
What is the difference between graph semi-supervised learning and normal semi-supervised learning?
The authors of your cited paper use the term graph-based semi-supervised learning (G-SSL) to refer to semi-supervised learning techniques which take graph structured data as their input.
Given their ...
- 1,011
1
vote
Accepted
How do I locate a specific object in an image?
so assuming your not allowed to use transfer methodologies (like take an already exisiting elephant object detector) my recommendation is to train a CNN classifier (labels are binary-- elephant exist, ...
- 2,359
1
vote
How to deal with a small amount of labeled samples?
In that particular competition, you can try using GAN to generate new data or adding noise to existing data. You can also use K-means algorithm. You can try using a smaller network and remove bias. ...
- 1,735
Only top scored, non community-wiki answers of a minimum length are eligible