Deep Clustering

72 papers with code • 3 benchmarks • 1 datasets

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Use these libraries to find Deep Clustering models and implementations
2 papers


Most implemented papers

Deep Clustering for Unsupervised Learning of Visual Features

facebookresearch/deepcluster 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.

Deep clustering: Discriminative embeddings for segmentation and separation

mpariente/asteroid 18 Aug 2015

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.

N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding

rymc/n2d 16 Aug 2019

We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.

Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks

snsun/pit-speech-separation 18 Mar 2017

We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on Non-negative Matrix Factorization (NMF) and Computational Auditory Scene Analysis (CASA), and compares favorably with Deep Clustering (DPCL) and the Deep Attractor Network (DANet).

Single-Channel Multi-Speaker Separation using Deep Clustering

JusperLee/Deep-Clustering-for-Speech-Separation 7 Jul 2016

In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation.

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

ratschlab/dpsom 3 Oct 2019

We show that DPSOM achieves superior clustering performance compared to current deep clustering methods on MNIST/Fashion-MNIST, while maintaining the favourable visualization properties of SOMs.

Structural Deep Clustering Network

bdy9527/SDCN 5 Feb 2020

The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.

Simple, Scalable, and Stable Variational Deep Clustering

king/s3vdc 16 May 2020

We then choose to focus on variational deep clustering (VDC) methods, since they mostly meet those criteria except for simplicity, scalability, and stability.

Dissimilarity Mixture Autoencoder for Deep Clustering

larajuse/DMAE 15 Jun 2020

The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture.

Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering

huitangtang/H-SRDC 8 Dec 2020

To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption.