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Deep Clustering Based on a Mixture of Autoencoders
Shlomo E. Chazan, Sharon Gannot, Jacob Goldberger
In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC) algorithm based on a mixture of deep autoencoders where each cluster is represented by an autoencoder. A clus…
Self-Normalization Properties of Language Modeling
Jacob Goldberger, Oren Melamud
Self-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. In the context of language modeling, this…
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
Yaniv Shachor, Hayit Greenspan, Jacob Goldberger
In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, w…
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Maayan Frid-Adar, Idit Diamant, Eyal Klang +3
Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using lar…
Clustering-driven Deep Embedding with Pairwise Constraints
Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger +1
Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have b…
Speech Dereverberation Using Fully Convolutional Networks
Ori Ernst, Shlomo E. Chazan, Sharon Gannot +1
Speech derverberation using a single microphone is addressed in this paper. Motivated by the recent success of the fully convolutional networks (FCN) in many image processing appli…