CNN
Convolutional neural network, sharing weights across positions to exploit spatial structure.
Resumen
A convolutional neural network applies the same small filter at every position of an input, so it learns a pattern once and recognises it anywhere. It solves the parameter explosion that a fully connected layer would suffer on an image, where each pixel would need its own weight. It exists because locality and translation invariance are true of images and largely untrue of language, which is why vision and language ended up with different architectures.
Grafo de tecnologías
Cómo se conecta CNN con el resto del stack.
Usa y depende de
- Neural Network concept Algorithms CNN based on Neural Network High ✓ source
Aplicado a
- Image Generation application Applications CNN used for Image Generation Medium ✓ source
Alternativas
Comparativas
Comparativas detalladas con las alternativas anteriores.