The landscape of computer vision is evolving rapidly, driven by the increasing complexity of tasks such as image classification, object detection, and facial recognition. However, traditional methods that rely on vast datasets and significant computational resources are becoming inefficient. In response to these challenges, Rathish Mohan, a visionary in artificial intelligence, introduces the concept of transfer learning, which significantly enhances efficiency and performance in computer vision tasks. He has extensively researched the potential of transfer learning to overcome existing limitations, making it an essential tool for the future of AI-driven technologies.
Overcoming Data Challenges in Computer Vision
One of the most significant barriers to effective computer vision training is the need for enormous amounts of labeled data. Traditional deep learning models require millions of labeled images to achieve high accuracy, which is both time-consuming and expensive. Transfer learning provides an innovative solution by using pre-trained models that have already learned basic features from large datasets. These …