|
|
|
|
|
Peng Chen and Huibing Wang
Semi-supervised metric learning intends to learn a distance function from the limited labeled data as well as a large amount of unlabeled data to better gauge the similarities of any two instances than using a general distance function. However, most exi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ali Reza Sajun and Imran Zualkernan
Given recent advances in deep learning, semi-supervised techniques have seen a rise in interest. Generative adversarial networks (GANs) represent one recent approach to semi-supervised learning (SSL). This paper presents a survey method using GANs for SS...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ilias Theodorakopoulos, Foteini Fotopoulou and George Economou
In this work, we propose a mechanism for knowledge transfer between Convolutional Neural Networks via the geometric regularization of local features produced by the activations of convolutional layers. We formulate appropriate loss functions, driving a ?...
ver más
|
|
|
|
|
|