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Found 14 papers, 5 papers with code
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models
Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models.
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor
Assuming that all clients have a single shared sample for each class, the knowledge anchor is constructed before each local training stage by extracting shared samples for missing classes and randomly selecting one sample per class for non-dominant classes.
Label Information Bottleneck for Label Enhancement
In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE.
Semantically Consistent Multi-view Representation Learning
In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner.
Multi-view Semantic Consistency based Information Bottleneck for Clustering
In this way, the learned semantic consistency from multi-view data can improve the information bottleneck to more exactly distinguish the consistent information and learn a unified feature representation with more discriminative consistent information for clustering.
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering
Further, we designed a multi-level consistency collaboration strategy, which utilizes the consistent information of semantic space as a self-supervised signal to collaborate with the cluster assignments in feature space.
Ranked #1 on
Multiview Clustering
on Fashion-MNIST
Multi-view Subspace Clustering Networks with Local and Global Graph Information
Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.
Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering
It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint.
Multi-view Hierarchical Clustering
This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data.
Bidirectional Loss Function for Label Enhancement and Distribution Learning
More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.
Generalized Label Enhancement with Sample Correlations
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.
Consistent and Complementary Graph Regularized Multi-view Subspace Clustering
The complementary graph regularizer investigates the specific information of multiple views.
Constrained Bilinear Factorization Multi-view Subspace Clustering
Multi-view clustering is an important and fundamental problem.
Feature Concatenation Multi-view Subspace Clustering
To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.
