Why Self-Supervised Learning is the Future of Computer Vision?
Presenting business cases where self-supervised learning in Computer vision is a game-changer and an introduction to Dino the state-of-the-art self-supervised learning model based on transformers.
Computer vision complexity is often related to data. Sometimes, collecting data is a heavy task but in many cases data is available but it is not well exploited. In the project I am working on, I have more than 1M images. That’s pretty cool! But labeling this many images means a lot of time and/or money. In this article, I will talk about DINO, a Self-supervised learning (or SSL) model based on Vision Transformer (ViT). This model stands out compared to convolutional networks.
The data boom and self-supervised learning
The volume of data created, captured, copied, and consumed globally has increased exponentially over the past 20 years. According to a Statista study, this volume is forecast to increase from approximately 64ZB in 2020 to 181 ZB in 2025.
Considering the volume of data we have today and how fast it is increasing, annotating all unstructured data would be practically impossible. Besides, in several fields, labeling data requires sharp business knowledge that is specific to experts. For example, in the field of radiology trained physicians need to assess various medical images and report the results to detect, characterize, and monitor diseases.
In such a high potential domain as AI, labeling data should not be a barrier to building intelligent models. In deep learning, we frequently compare Neural Networks to the human brain’s way of learning. But as humans, we don’t need to see several labeled images of the same object to learn what it is. As explained in the Meta AI article, “Common sense helps people learn new skills without requiring massive amounts of teaching for every single task.” a human can recognize an object after seeing it even without putting a label on it. Self-supervised learning may be a solution to getting closer to the common sense we humans have.
Self-supervised learning business use cases
Concretely SSL may be convenient for several business cases where getting data is relatively easy. To name a few, I think that self-supervised learning is particularly adapted to the industrial sector and retail.
Industry
Read the full article on Sicara’s blog
