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Found 8 papers, 1 papers with code
Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process
Single-cell RNA sequencing (scRNA-seq) is powerful technology that allows researchers to understand gene expression patterns at the single-cell level.
Towards Understanding the Cause of Error in Few-Shot Learning
We first introduce and derive a theoretical upper bound of error rate which is constrained to 1) linear separability in the learned embedding space and 2) discrepancy of task-specific and task-independent classifier.
BiOpt: Bi-Level Optimization for Few-Shot Segmentation
On each task, the inner loop aims to learn optimized prototypes from the query images.
Prototype Refinement Network for Few-Shot Segmentation
Furthermore, to extract representative prototypes of the new classes, we use adaptation and fusion for prototype refinement.
Generalized Adaptation for Few-Shot Learning
Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model.
Prototype Rectification for Few-Shot Learning
Few-shot learning requires to recognize novel classes with scarce labeled data.
Hierarchical Attention Networks for Medical Image Segmentation
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.
Imagination Based Sample Construction for Zero-Shot Learning
Different from these existing types of methods, we propose a new method: sample construction to deal with the problem of ZSL.
