Скачать или смотреть CNNs Are NOT Dead: Why Convolutional Neural Networks Still Win in Edge AI and Hybrid Models

Convolutional Neural Networks (CNNs) revolutionized AI, powering everything from face recognition to medical diagnostics. But with the rise of Vision Transformers (ViTs) and large multimodal models, are CNNs obsolete? Absolutely not.

In this deep dive, we explore why the foundational efficiency and translation-invariance of CNNs make them essential for 2025's toughest challenges: Edge Computing, real-time video processing, and low-data medical imaging. We'll break down the latest innovations that are keeping the "Convolution" layer at the heart of modern AI:

The ViT/CNN Hybrid: How architectures like ConvNeXt are merging the best of both worlds for peak performance.

Edge Optimization: Techniques like depthwise separable convolutions (MobileNet) and model quantization that make CNNs lightning-fast on mobile and IoT devices.

Beyond the Image: Exciting applications in analyzing sequential data like time-series, audio, and even genomic sequences.

Explainability (XAI): Using techniques to open the black box and see why a CNN makes a decision, crucial for high-stakes fields like healthcare.

Mastering CNNs is not just knowing history—it’s mastering the bedrock of real-world AI deployment today.

#CNNs #DeepLearning #ComputerVision #EdgeAI #MachineLearning #VisionTransformers #AIArchitecture