There are 4 repositories under deep-clustering topic.
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
[AAAI 2023] An official source code for paper Hard Sample Aware Network for Contrastive Deep Graph Clustering.
[AAAI 2022] An official source code for paper Deep Graph Clustering via Dual Correlation Reduction.
Pytorch implements Deep Clustering: Discriminative Embeddings For Segmentation And Separation
Papers for Open Knowledge Discovery
A pytorch implementation of the paper Unsupervised Deep Embedding for Clustering Analysis.
This project is a scalable unified framework for deep graph clustering.
A very simple self-supervised image classification framework!
Official PyTorch implementation of 🏁 MFCVAE 🏁: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of variational autoencoders to find multiple disentangled clusterings of data.
The code of AGCN (Attention-driven Graph Clustering Network), which is accepted by ACM MM 2021.
Graph Agglomerative Clustering (GAC) toolbox
Author implementation of deep clustering model from the paper "Learning Embedding Space for Clustering From Deep Representations".
Graph Agglomerative Clustering Library
[BMVC2023] Official code for TEMI: Exploring the Limits of Deep Image Clustering using Pretrained Models
Official implementation for [N2DCX] Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
Source code for E2DTC: An End to End Deep Trajectory Clustering Framework via Self-Training. ICDE 2021.
Course project for EE698R (2020-21 Sem 2). An X-Vector Based Speaker Diarization System with AutoEncoder based clustering method. Also supports spectral and KMeans clustering method.
TensorFlow implementation of the Dissimilarity Mixture Autoencoder: https://arxiv.org/abs/2006.08177
This is a project for Columbia Research Project
The collection and reproduction code of the clustering methods I have known
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder
Submission for DS 2020
HyperTrack: Neural Combinatorics for High Energy Physics [arXiv:2309.14113]
Deep clustering for relation extraction
Discriminately Boosted Clustering (DBC) builds on DEC by using convolutional autoencoder instead of feed forward autoencoder. It uses the same training scheme, reconstruction loss and cluster assignment hardening loss as DEC. DBC achieves good results on image datasets because of its use of convolutional neural network.
Transfer and adaptation of general characteristics without supervision in microscopy images.
Suitable Agriculture Land Detection from Satellite Imaginary with Deep Clustering
Deep Clustering for TW Determination of an ERP Component
Jupyter notebooks for predicting tides, using unsupervised neural net clustering.
This repository contains an implementation of a deep learning architecture designed for unsupervised or self-supervised classification tasks. The architecture consists of two components: a classifier and an aligner.