Deep Clustering
115 papers with code • 5 benchmarks • 2 datasets
Libraries
Use these libraries to find Deep Clustering models and implementations
MNIST
USPS
Most implemented papers
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
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.
Attention-driven Graph Clustering Network
The combination of the traditional convolutional network (i. e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.
Deep Embedded K-Means Clustering
spdj2271/DEKM
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To this end, we discard the decoder and propose a greedy method to optimize the representation.
Multi-Class Cell Detection Using Spatial Context Representation
topoxlab/mcspatnet
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ICCV 2021
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks.
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
KevinWangHP/Anomaly-Clustering
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We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.
Twin Contrastive Learning for Online Clustering
XLearning-SCU/2022-IJCV-TCL
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Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
JusperLee/UtterancePIT-Speech-Separation
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We propose a novel deep learning model, which supports permutation invariant training (PIT), for speaker independent multi-talker speech separation, commonly known as the cocktail-party problem.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments.
Object category learning and retrieval with weak supervision
StevenHickson/VideoObjectProposals
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We consider the problem of retrieving objects from image data and learning to classify them into meaningful semantic categories with minimal supervision.
Alternative Objective Functions for Deep Clustering
mpariente/asteroid
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ICASSP 2018
The recently proposed deep clustering framework represents a significant step towards solv-ing the cocktail party problem.
