Multi-modal data clustering using deep learning: A systematic review
Multi-modal clustering represents a formidable challenge in the domain of unsupervised
learning. The objective of multi-modal clustering is to categorize data collected from diverse …
learning. The objective of multi-modal clustering is to categorize data collected from diverse …
On the effects of self-supervision and contrastive alignment in deep multi-view clustering
Self-supervised learning is a central component in recent approaches to deep multi-view
clustering (MVC). However, we find large variations in the development of self-supervision …
clustering (MVC). However, we find large variations in the development of self-supervision …
CONAN: contrastive fusion networks for multi-view clustering
G Ke, Z Hong, Z Zeng, Z Liu, Y Sun… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
With the development of big data, deep learning has made remarkable progress on multi-
view clustering. Multi-view fusion is a crucial technique for the model obtaining a common …
view clustering. Multi-view fusion is a crucial technique for the model obtaining a common …
Multiple graphs learning with a new weighted tensor nuclear norm
As an effective convex relaxation of the rank minimization model, the tensor nuclear norm
minimization based multi-view clustering methods have been attracting more and more …
minimization based multi-view clustering methods have been attracting more and more …
Bassnet: A variational gated autoencoder for conditional generation of bass guitar tracks with learned interactive control
Deep learning has given AI-based methods for music creation a boost by over the past
years. An important challenge in this field is to balance user control and autonomy in music …
years. An important challenge in this field is to balance user control and autonomy in music …
AMCFCN: attentive multi-view contrastive fusion clustering net
H **ao, Z Hong, L **ong, Z Zeng - PeerJ Computer Science, 2024 - peerj.com
Advances in deep learning have propelled the evolution of multi-view clustering techniques,
which strive to obtain a view-common representation from multi-view datasets. However, the …
which strive to obtain a view-common representation from multi-view datasets. However, the …
A multimodal clustering framework with cross reconstruction autoencoders
Q Zhao, L Zong, X Zhang, Y Li, X Tang - IEEE Access, 2020 - ieeexplore.ieee.org
Multimodal clustering algorithms partitions a multimodal dataset into disjoint clusters.
Common feature extraction is a key part in multimodal clustering algorithms. Recently, deep …
Common feature extraction is a key part in multimodal clustering algorithms. Recently, deep …
Multimodal Isotropic Neural Architecture with Patch Embedding
H Truchan, E Naumov, R Abedin, G Palmer… - … Conference on Neural …, 2023 - Springer
Patch embedding has been a significant advancement in Transformer-based models,
particularly the Vision Transformer (ViT), as it enables handling larger image sizes and …
particularly the Vision Transformer (ViT), as it enables handling larger image sizes and …
On the Role of Self-supervision in Deep Multi-view Clustering
Self-supervised learning is a central component in many recent approaches to deep multi-
view clustering (MVC). However, we find large variations in the motivation and design of self …
view clustering (MVC). However, we find large variations in the motivation and design of self …
Improving Representation Learning for Deep Clustering and Few-shot Learning
DJ Trosten - 2023 - munin.uit.no
The amounts of data in the world have increased dramatically in recent years, and it is
quickly becoming infeasible for humans to label all these data. It is therefore crucial that …
quickly becoming infeasible for humans to label all these data. It is therefore crucial that …