SSL, or semi-supervised learning, is applied in computer vision tasks to enhance model performance by utilizing both labeled and unlabeled data. In traditional machine learning, models typically rely on large amounts of labeled data for training. However, acquiring labeled data can be labor-intensive and expensive. Semi-supervised learning addresses this challenge by allowing developers to leverage a smaller set of labeled images along with a larger set of unlabeled images. This combination helps models generalize better and improves their ability to make predictions on unseen data.
A common approach in SSL for computer vision is to first train the model on the limited labeled dataset. After this initial training, the model is then applied to the unlabeled dataset to generate pseudo-labels. These pseudo-labels act as a form of additional supervision. For instance, if a developer is working on an image classification task, they might initially label a few hundred images, then use the trained model to predict labels for thousands of unlabeled images. The model is retrained using both the original labeled images and the newly labeled pseudo-images, enhancing its capability to recognize patterns and features present in the data.
Some specific techniques used in SSL include consistency training and data augmentation. In consistency training, a model is encouraged to produce similar outputs when presented with slightly altered versions of the same input image, such as different lighting conditions or rotations. Data augmentation helps improve the robustness of the model by artificially expanding the training set with varied versions of the same images. By combining these strategies, SSL effectively improves performance in tasks like object detection and facial recognition, making it an appealing choice for developers aiming to build efficient computer vision systems without needing vast labeled datasets.