Deep Clustering
115 papers with code • 5 benchmarks • 2 datasets
Libraries
Use these libraries to find Deep Clustering models and implementations
MNIST
USPS
Latest papers with no code
GCC: Generative Calibration Clustering
Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space.
Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Two key issues are identified: 1) the curse of dimensionality when applying these measures to raw data, and 2) the unreliable comparison of clustering results across different embedding spaces stemming from variations in training procedures and parameter settings in different clustering models.
Incorporating Higher-order Structural Information for Graph Clustering
In recent years, graph convolutional network (GCN) has emerged as a powerful tool for deep clustering, integrating both graph structural information and node attributes.
Self-Supervised Facial Representation Learning with Facial Region Awareness
Recent efforts toward this goal are limited to treating each face image as a whole, i. e., learning consistent facial representations at the image-level, which overlooks the consistency of local facial representations (i. e., facial regions like eyes, nose, etc).
Towards Calibrated Deep Clustering Network
Deep clustering has exhibited remarkable performance; however, the overconfidence problem, i. e., the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy, has been overlooked in prior research.
Imbalanced Data Clustering using Equilibrium K-Means
Traditional centroid-based clustering algorithms, such as hard K-means (HKM, or Lloyd's algorithm) and fuzzy K-means (FKM, or Bezdek's algorithm), display degraded performance when true underlying groups of data have varying sizes (i. e., imbalanced data).
Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters
Soft silhouette rewards compact and distinctly separated clustering solutions like the conventional silhouette coefficient.
Towards Image Semantics and Syntax Sequence Learning
To mitigate this gap, we introduce the concept of "image grammar", consisting of "image semantics" and "image syntax", to denote the semantics of parts or patches of an image and the order in which these parts are arranged to create a meaningful object.
Deep Embedding Clustering Driven by Sample Stability
To address this issue, we propose a deep embedding clustering algorithm driven by sample stability (DECS), which eliminates the requirement of pseudo targets.
Consistency Enhancement-Based Deep Multiview Clustering via Contrastive Learning
Furthermore, the representation process for clustering is enhanced through spectral clustering, and the consistency across multiple views is improved.
