SIFT
Deep Learning
Neural networks that learn to compare two inputs and determine their similarity
Detailed Explanation
Siamese Networks are a type of neural network architecture designed to compare two inputs and assess their similarity. Comprising twin subnetworks with shared weights, they extract features from each input. The network then computes a distance metric between these features to determine how closely related the inputs are, commonly used in face verification, signature matching, and other similarity-based tasks.
Use Cases
- •Use case: Facial recognition system verifying identities by comparing a new image with stored profiles for authentication.
Related Terms
Other terms in the Deep Learning category