Issues in Training: Generator Diminished Gradient: Discriminator gets too successful that the generator gradient vanishes and learns nothing. As seen earlier the training of a GAN is typically alternating between Gradient Ascent on Discriminator and Gradient Descent on Generator. But optimizing GD on generator doesn’t work well. Consider the above Read more…
Face Recognition: Before looking into what face recognition is and how it works, let us understand the difference between face recognition and face verification. Face Verification checks “is this the claimed person?”. For example, in school, you go with your ID card and the invigilator verifies your face with the ID Read more…
GAN are Generative Adversarial networks. GANs are neural networks that generate synthetic data given certain input data. The main goal is unsupervised sampling from complex high dimensional distribution. And this is done by taking samples from random noise and learn the transformation to input distribution
Introduction: Face Recognition problems can be broadly classified into two categories ⦁ Face Verification: Identifying if the given face is of the claimed person ⦁ Face Recognition: Identifying different instances (of faces) of the claimed person Other type of problems includes Clustering (grouping similar faces together) Simple Techniques for face Read more…