Groups
Category
Triplet loss and contrastive loss are metric-learning objectives that teach a model to map similar items close together and dissimilar items far apart in an embedding space.
GANs frame learning as a two-player game where a generator tries to fool a discriminator, and the discriminator tries to detect fakes.