Deep Clustering

51 papers with code • 2 benchmarks • 0 datasets

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Greatest papers with code

Online Deep Clustering for Unsupervised Representation Learning

open-mmlab/OpenSelfSup CVPR 2020

In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly.

Clustering Deep Clustering +1

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.

Ranked #15 on Image Clustering on CIFAR-10 (using extra training data)

Clustering Deep Clustering +1

Alternative Objective Functions for Deep Clustering

mpariente/asteroid ICASSP 2018

The recently proposed deep clustering framework represents a significant step towards solv-ing the cocktail party problem.

Clustering Deep Clustering +1

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.

Clustering Deep Clustering +2

DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps

ratschlab/SOM-VAE 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.

Clustering Deep Clustering +4

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).

Clustering Deep Clustering +1

Structural Deep Clustering Network

461054993/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.

Clustering Deep Clustering +1

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.

Clustering Deep Clustering +4

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.

Clustering Deep Clustering +3

Deep Comprehensive Correlation Mining for Image Clustering

Cory-M/DCCM ICCV 2019

Recent developed deep unsupervised methods allow us to jointly learn representation and cluster unlabelled data.

Clustering Deep Clustering +1