Semi-Supervised Learning Explained with Cats
Key Points
- Supervised learning trains a model on a fully labeled dataset (e.g., cat vs. dog images) by iteratively adjusting weights to minimize prediction errors.
- Creating these labels—especially for tasks like image segmentation, genetic sequencing, or protein classification—is time‑consuming, labor‑intensive, and often requires specialized expertise.
- Semi‑supervised learning addresses this bottleneck by leveraging a small amount of labeled data together with abundant unlabeled data to improve model performance.
- Using only limited labeled data can lead to poor generalization, so semi‑supervised approaches help mitigate overfitting and make better use of available information.
Sections
- Explaining Semi‑Supervised Learning with Cats & Dogs - The speaker introduces supervised learning by detailing how a model classifies labeled cat and dog images, then highlights the reliance on labeled data as a prelude to the concept of semi‑supervised learning.
- Combating Overfitting with Unlabeled Data - The speaker explains that relying solely on a small labeled dataset causes models to overfit—learning spurious cues like indoor vs. outdoor settings—and that semi‑supervised learning mitigates this by augmenting training with many unlabeled examples to improve generalization.
- Iterative Pseudo-Labeling and Clustering Techniques - The speaker explains three semi‑supervised strategies—iterative retraining with pseudo‑labels, auto‑encoder based feature extraction, and clustering‑based pseudo‑label assignment—to improve model performance with limited labeled data.
- Semi‑Supervised Learning Explained - The speaker describes semi‑supervised learning as a method that blends labeled and unlabeled data to train a better‑fitting model, using the analogy of raising a pet that requires both structure and freedom.
Full Transcript
# Semi-Supervised Learning Explained with Cats **Source:** [https://www.youtube.com/watch?v=C3Lr6Waw66g](https://www.youtube.com/watch?v=C3Lr6Waw66g) **Duration:** 00:10:02 ## Summary - Supervised learning trains a model on a fully labeled dataset (e.g., cat vs. dog images) by iteratively adjusting weights to minimize prediction errors. - Creating these labels—especially for tasks like image segmentation, genetic sequencing, or protein classification—is time‑consuming, labor‑intensive, and often requires specialized expertise. - Semi‑supervised learning addresses this bottleneck by leveraging a small amount of labeled data together with abundant unlabeled data to improve model performance. - Using only limited labeled data can lead to poor generalization, so semi‑supervised approaches help mitigate overfitting and make better use of available information. ## Sections - [00:00:00](https://www.youtube.com/watch?v=C3Lr6Waw66g&t=0s) **Explaining Semi‑Supervised Learning with Cats & Dogs** - The speaker introduces supervised learning by detailing how a model classifies labeled cat and dog images, then highlights the reliance on labeled data as a prelude to the concept of semi‑supervised learning. - [00:03:10](https://www.youtube.com/watch?v=C3Lr6Waw66g&t=190s) **Combating Overfitting with Unlabeled Data** - The speaker explains that relying solely on a small labeled dataset causes models to overfit—learning spurious cues like indoor vs. outdoor settings—and that semi‑supervised learning mitigates this by augmenting training with many unlabeled examples to improve generalization. - [00:06:23](https://www.youtube.com/watch?v=C3Lr6Waw66g&t=383s) **Iterative Pseudo-Labeling and Clustering Techniques** - The speaker explains three semi‑supervised strategies—iterative retraining with pseudo‑labels, auto‑encoder based feature extraction, and clustering‑based pseudo‑label assignment—to improve model performance with limited labeled data. - [00:09:39](https://www.youtube.com/watch?v=C3Lr6Waw66g&t=579s) **Semi‑Supervised Learning Explained** - The speaker describes semi‑supervised learning as a method that blends labeled and unlabeled data to train a better‑fitting model, using the analogy of raising a pet that requires both structure and freedom. ## Full Transcript
What is semi-supervised learning?
Well, let me give you an example.
So consider building an AI model that can classify pictures of cats and dogs.
If you give the model a picture of an animal, it will tell you if that picture shows a cat or if it shows a dog.
Now, we can build a model like that using a process called supervised learning, not semi just supervised learning.
Now, this involves training the model on a data set.
And this dataset has images where they are labeled and those images are labeled as either cat or dog.
So for instance, we might have 100 images and half of them are labeled as cat and the other half of them are labeled as dog.
Now we also have here an AI model that is going to do the work here.
And the model learns from these labeled examples by
identifying patterns and features that differentiate these animals.
So perhaps things like a shape because they're generally more pointy for cats
or body structure, which are generally more bulky for dogs.
Then during training, the model makes predictions,
evaluates the accuracy of the predictions through something called a loss function, which says, Was I right?
Was it really a cat or a dog?
And it makes adjustments using techniques such as gradient
descent that update the model weights to improve future predictions.
Now that's all well and good.
But as we have established, supervised learning needs a labeled dataset.
These this dataset here is full of labels and those form the ground truth from which the model trains.
Now, when we think about what a label actually is, it could be as simple as a classification label.
So that just says that, yeah, this picture contains a cat.
And yeah, this picture contains a dog,
but it could also be something a bit more complicated as well, such as an image segmentation label,
and that assigns labels to individual pixel boundaries
in an image indicating precisely where in the image the object can be found.
Now, this is a manual work that somebody has to perform, and I don't know about you,
but going through a dataset of hundreds of images of pets and then like
designing and assigning labels to them, it's not my idea of a good time.
Labeling images of cats and dogs is time consuming and tedious,
but,
but what about more specialized use cases like genetic sequencing or protein classification?
That sort of data annotation is not only extremely time consuming, but it also requires a very specific domain expertise.
There are just fewer people that can do it.
So enter the world of semi-supervised learning to help us out.
Semi-supervised learning offers a way to extract benefit from a scarce amount of
labeled data while making use of relatively abundant, unlabeled data.
Now, before we get into the how, as in how semi supervised learning works, let's first address the why.
Why not just build your model using whatever label data is currently available?
Well, the answer to that is that using a limited amount of label data introduces the possibility of something called overfitting.
This happens when the model performs well on the training dataset, but it struggles to generalize to new unseen images.
So for instance, suppose that in the training dataset,
most of the images are the cats that are taken indoors and most of the images of dogs, those are taken outdoors.
Well, the model might mistakenly learn to associate outdoor settings
with dogs rather than focusing on more meaningful features, and as a result, it could incorrectly classify
any image of its outdoors as being a dog, even if it contains a cat.
In general, the solution to overfitting is to increase the size of the training dataset,
and that is where semi supervised learning comes in by incorporating unlabeled data into the training process.
We can effectively expand our data set.
So, for example, instead of just training on a model that contains 100
labeled examples, we can also add in some unlabeled examples as well.
So maybe we could add in 1000 unlabeled examples into this dataset as well.
That gives the model more context to learn from without requiring additional labeled data.
So that's the why.
Now let's get into the how.
Now, there are many semi-supervised learning techniques as just narrowly focused for them.
So first up is something called the wrapper method.
So what is the wrapper method?
Well, here's what it does.
We start with the base model trained on a labeled data set,
and then we use this train model to predict labels for
the unlabeled dataset. That's images that contain, let's say, cats and dogs,
but the individual images do not specify an actual label.
Now, those predicted labels, they have a name.
They are called pseudo-labels.
So a pseudo-label is a label assigned by this method and they are typically probabilistic rather than deterministic,
meaning that the pseudo label, it comes with a probability of how confident the model is in its labeling.
So it might say, for example, for a given label, there's an 85% chance that this one is a dog, for example.
Now pseudo labels with high confidence are then combined with the
original label dataset and then they're treated as if they were actual ground truth labels.
Now the model is retrained on this new dataset, which is of course now a bit larger,
and that includes both the label and the pseudo-label data.
And this process can be repeated iteratively with each iteration, improving the quality of the pseudo-labels
as the model becomes better at distinguishing between the images.
So that's the wrapper method.
Now another approach is called unsupervised pre-processing.
Now that uses a feature called an auto encoder.
Now, what the auto encoder does is it learns to represent each image
in a more compact and meaningful way by capturing essential features, so things like edges and shapes and textures.
And when that's applied to the unlabeled images, it extracts these
key features which are then used to train a supervised model more effectively.
Therefore, it's helping it better generalize even with limited label data.
Another method that is commonly used relates to clustering.
So clustering based methods.
Now these apply the cluster assumption, which is essentially that similar data points are likely to belong to the same class.
So a clustering algorithm, something like K-means can group all data points, both labeled
and unlabeled into clusters based on their similarity.
So, for example, if we do that here,
if we've got a cluster and we've got some labeled examples that kind of fall here on the matrix,
nd then we have some unlabeled examples which fall around here as well.
Well, we can pseudo label the unlabeled images in that cluster as well.
So if the labeled images were cats, we could say those unlabeled ones that fall in the same area are cats as well.
And then finally, the method we want to talk about here is called active learning.
Now what active learning does is brings humans into the loop.
So samples with low confidence level pseudo labels, meaning the model wasn't really sure how to classify them.
They can be referred to human annotators for labeling.
So human labels are only working on images that the model is unable to reliably classify itself.
Now there are other semi supervised learning techniques as well, but the key here is that they can be combined.
So, for example, we could start here with unsupervised pre-processing,
and that could be used to first extract meaningful features from the unlabeled dataset,
which gives us a solid foundation for more accurate clustering based methods that we can then use.
Now these clusters of pseudo-labeled data can then be incorporated into the
wrapper method up here, improving the model with each retraining cycle,
and then meanwhile, we would then rely on active learning to take the most ambiguous of the most low confidence samples
and ensure that human effort is focused where it's most needed.
So that is semi supervised learning
a method to incorporate unlabeled data into the model training alongside labeled examples creating a better fitting model.
Just like raising a cat or a dog it needs a little bit of structure, a little bit of freedom and a whole lot of learning along the way.