3 accepted papers
Annotation of biomedical databases by clinicians is a very difficult, sometimes imprecise, and time consuming task. An alternative is to ask the clinician expert for the annotations they are the most confident in, which results in a semi-supervised classification problem. In this paper, we present a…
Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy comput…
This paper concerns the supervised generative non parametric autoencoder. Classical methods are based on variational non supervised autoencoders (VAE). Variational autoencoders encourage the latent space to fit a prior distribution, like a Gaussian. However, they tend to draw stronger assumptions fo…