Self-Supervised Learning in Computer Vision: Uncovering Visual Representations
Computer vision, the field dedicated to enabling computers to interpret and understand visual information from the world, has been dramatically transformed by the advent of deep learning. Within deep learning, self-supervised learning has emerged as a potent technique for uncovering meaningful visual representations from unlabeled data. In this blog, we’ll delve into the world of self-supervised learning in computer vision, explore its principles, understand its significance, and discuss its applications.
Understanding Self-Supervised Learning
Traditionally, supervised learning in computer vision relies on labeled datasets, where every image or video frame is paired with corresponding annotations, such as object categories or segmentation masks. However, collecting and annotating large datasets is costly and time-consuming.
Self-supervised learning, in contrast, leverages unlabeled data, which is abundant and easier to obtain. It transforms an unsupervised learning problem into a supervised one by creating surrogate tasks or objectives from the data itself.
Key components of self-supervised learning in computer vision include:
- Data Augmentation: Images or video frames are transformed into multiple variations using data augmentation techniques, such as cropping, rotation, color jittering, or flipping.
- Contrastive Objective: A…
