Semi-supervised Image Classification
Annotating data can be costly, and large corpuses of carefully labeled data are not always available. An important domain of study in Deep Learning is thus to learn as much as posible from unlabelled data. This is exactly what you are going to do in this project.
We will use the Stanford STL-10 dataset. It is composed of 100k unlabeled images along with 500 labeled training images per class mapped to 10 pre-defined training folds of 1k images. Standard testing protocol consists of training 10 different models on each of the 10 predefined folds of data, and report average accuracy on full test set. However, for compute reasons, you will only use the labeled pre-defined fold with id 0. The full test set is made of 8k images.
In the context of this project, you cannot use models that were pre-trained on other datasets and you will not be able to use any additional data.
The following code allows you to access the only data you can use during the project:
import torchvision
# Train data
unlabeled_train_set = torchvision.datasets.STL10(root='./data', split='unlabeled', download=True)
labeled_train_set = torchvision.datasets.STL10(root='./data', split='train', folds=0, download=True)
# Test data (to report model performance)
test_set = torchvision.datasets.STL10(root='./data', split='test', download=True)
You should have 100k unlabeled training images, 1k labeled training images (from fold 0) and 8k test images.
Important note: It is of course forbidden to validate your models on test data.
At the beginning of the project, you should spend some time on the litterature. Each member in the group should select and read at least one research paper that is relevant and presents a method that could be applied to your problem.
After becoming more familiar with the literature, an interesting first step is often to implement a baseline approach, i.e. a method that is as simple as possible and that solves the task reasonably well. This will be your comparison point for new ideas you might want to try.
You can choose among 3 options: