The action of acquiring the STL-10 image collection involves retrieving a pre-existing set of labeled images specifically designed for developing unsupervised feature learning, deep learning, and self-supervised learning algorithms. A typical scenario includes accessing the dataset files, usually through a dedicated website or repository, and transferring them to a local machine or cloud storage for use in model training and evaluation.
Obtaining this particular image resource is beneficial for researchers and practitioners because it offers a standardized benchmark for assessing novel machine learning techniques. Its relevance stems from its structure: a relatively small set of labeled images paired with a significantly larger set of unlabeled images. This characteristic allows researchers to explore semi-supervised learning paradigms effectively. Furthermore, its establishment provides a comparative basis against which new methodologies can be rigorously evaluated.
