Clustering
2478 papers with code • 0 benchmarks • 4 datasets
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TIMIT
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Most implemented papers
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE).
Deep Clustering for Unsupervised Learning of Visual Features
facebookresearch/deepcluster
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ECCV 2018
In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
Deep clustering: Discriminative embeddings for segmentation and separation
The framework can be used without class labels, and therefore has the potential to be trained on a diverse set of sound types, and to generalize to novel sources.
Entity Embeddings of Categorical Variables
entron/entity-embedding-rossmann
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As entity embedding defines a distance measure for categorical variables it can be used for visualizing categorical data and for data clustering.
Leveraging BERT for Extractive Text Summarization on Lectures
This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection.
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation.
A high-bias, low-variance introduction to Machine Learning for physicists
The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists.
ClusterGAN : Latent Space Clustering in Generative Adversarial Networks
eriklindernoren/PyTorch-GAN
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While one can potentially exploit the latent-space back-projection in GANs to cluster, we demonstrate that the cluster structure is not retained in the GAN latent space.
Sampling Matters in Deep Embedding Learning
CompVis/metric-learning-divide-and-conquer
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ICCV 2017
In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions.
