methodology-6

Clustering

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in clustering-6

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Libraries

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Use these libraries to find clustering-6 models and implementations

elki-project/elki

17 papers 779

Datasets

TIMIT

Furit360

Daily and Sports Activities

MapReader Data

Subtasks

Constrained Clustering

Most implemented papers

FaceNet: A unified embedding for face recognition and clustering

James Philbin, Florian Schroff, Dmitry Kalenichenko•Wed Mar 11 2015

A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.

14102

Content

benedekrozemberczki/karateclub

12 papers 2,113

yueliu1999/awesome-deep-graph-clust…

6 papers 753

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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Iryna Gurevych, Nils Reimers•Tue Aug 13 2019

Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity is presented.

16007 0

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Adversarial Autoencoders

I. Goodfellow, Jonathon Shlens, N. Jaitly, Alireza Makhzani•Tue Nov 17 2015

It is shown how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization.

2343 0

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XGBoost: A Scalable Tree Boosting System

Tianqi Chen, Carlos Guestrin•Tue Mar 08 2016

This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.

48813 0

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SOLO: Segmenting Objects by Locations

Chunhua Shen, Tao Kong, Xinlong Wang, Lei Li, Yuning Jiang•Mon Dec 09 2019

A new, embarrassingly simple approach to instance segmentation in images by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location and size thus nicely converting instance mask segmentation into a classification-solvable problem.

790 0

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Unsupervised Deep Embedding for Clustering Analysis

Junyuan Xie, Ross B. Girshick, Ali Farhadi•Wed Nov 18 2015

Deep Embedded Clustering is proposed, a method that simultaneously learns feature representations and cluster assignments using deep neural networks and learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective.

3176 0

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Deep Speaker: an End-to-End Neural Speaker Embedding System

Xiangang Li, Zhenyao Zhu, Xuewei Zhang, Ying Cao, Chao Li, Xiaokong Ma, B. Jiang, Xiao Liu, Ajay Kannan•Thu May 04 2017

Results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition are presented, and it is suggested that Deep Speaker outperforms a DNN-based i-vector baseline.

521 0

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Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering

Bo Yang, Xiao Fu, N. Sidiropoulos, Mingyi Hong•Fri Oct 14 2016

A joint DR and K-means clustering approach in which DR is accomplished via learning a deep neural network (DNN) while exploiting theDeep neural network's ability to approximate any nonlinear function is proposed.

943 0

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CatBoost: unbiased boosting with categorical features

Gleb Gusev, L. Ostroumova, A. Vorobev, Anna Veronika Dorogush, Andrey Gulin•Tue Jun 27 2017

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit and provides a detailed analysis of this problem and demonstrates that proposed algorithms solve it effectively, leading to excellent empirical results.

4869 0

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Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

M. Jeon, Jiwon Jun, Yeongmin Ko, Donghwuy Ko•Sat Feb 15 2020

A traffic line detection method called Point Instance Network (PINet), based on the key points estimation and instance segmentation approach, which achieves competitive accuracy and false positive on the TuSimple and Culane datasets.

276 0

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