Convolutional Neural Network
A Convolutional Neural Network (CNN) is a deep learning architecture designed to process grid-like structured data, such as images or time-series signals.
How it works
- Convolutional layers: automatically learn hierarchical features (edges, shapes, textures).
- Pooling layers: downsample the data to make it computationally efficient.
- Fully connected layers: integrate extracted features for classification or prediction.
Applications
- Image recognition (e.g., cats vs. dogs).
- Object detection (self-driving cars, security cameras).
- Medical imaging (X-rays, MRIs).
- Natural language processing (character-level CNNs for text classification).
CNNs revolutionized computer vision by replacing handcrafted feature engineering with automatic representation learning. Instead of manually defining edges, textures, or shapes, the network learns filters that capture these patterns directly from data. This makes CNNs both powerful and flexible across domains.
A defining aspect of CNNs is parameter sharing: the same filter scans across an entire image, allowing the network to detect the same feature regardless of its position. Combined with pooling layers, this creates a form of translation invariance, making CNNs robust to small shifts or distortions.
Beyond vision, CNNs have expanded into speech recognition, natural language processing, and even financial time series, since their ability to capture local dependencies applies to any grid-like data. Recent innovations, such as residual networks (ResNets) or dilated convolutions, have pushed CNNs to deeper, more efficient architectures. Despite competition from Transformers, CNNs remain a cornerstone of deep learning due to their efficiency and interpretability in structured tasks.
Reference
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NeurIPS.
- Understanding Convolutional Neural Networks (CNN), Innovatiana
