Convolutional neural networks (CNNs) are impressive for their ability to automatically learn features from raw image data, enabling breakthroughs in computer vision tasks. Their layered architecture mimics the human visual system, processing images hierarchically from simple edges to complex objects.
CNNs have achieved remarkable results in applications like image classification (e.g., ImageNet), object detection (e.g., YOLO), and image generation (e.g., GANs). Their adaptability across domains, from medical imaging to autonomous vehicles, showcases their power.
While CNNs are highly effective, their complexity requires significant computational resources and large datasets for training. Despite these challenges, they remain a cornerstone of modern computer vision.