Deep Clustering

118 papers with code • 5 benchmarks • 2 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.

Leveraging tensor kernels to reduce objective function mismatch in deep clustering

DanielTrosten/DTKC 20 Jan 2020

In this work we study OFM in deep clustering, and find that the popular autoencoder-based approach to deep clustering can lead to both reduced clustering performance, and a significant amount of OFM between the reconstruction and clustering objectives.

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.

Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering

huitangtang/SRDC-CVPR2020 CVPR 2020

To alleviate this risk, we are motivated by the assumption of structural domain similarity, and propose to directly uncover the intrinsic target discrimination via discriminative clustering of target data.

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.