Non-Parametric Clustering Using Deep Neural Networks
In this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network i...
Main Authors: | Christos Avgerinos, Vassilios Solachidis, Nicholas Vretos, Petros Daras |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9171232/ |
Similar Items
-
Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks
by: Christos Avgerinos, et al.
Published: (2023-01-01) -
Advances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets
by: Thomas Kopalidis, et al.
Published: (2024-02-01) -
An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder
by: Mingxin Qiu, et al.
Published: (2024-03-01) -
Mining the colossal patterns using ISSA based KMC with VGHHO clustering model for high dimensional data
by: Sreenivasula Reddy T, et al.
Published: (2024-03-01) -
An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets
by: Ke Li, et al.
Published: (2007-10-01)