Search Results for author:
Found 10 papers, 3 papers with code
Identification of Pediatric Respiratory Diseases Using Fine-grained Diagnosis System
The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage.
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation
Pseudo labeling expands the number of ``labeled" samples in each class in the target domain, and thus produces a more robust and powerful cluster core for each class to facilitate adversarial learning.
Learning Open Set Network with Discriminative Reciprocal Points
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
Kernel Quantization for Efficient Network Compression
Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
P-ODN: Prototype based Open Deep Network for Open Set Recognition
Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically.
ODN: Opening the Deep Network for Open-set Action Recognition
Most of the existing action recognition works hold the \textit{closed-set} assumption that all action categories are known beforehand while deep networks can be well trained for these categories.
Joint Network based Attention for Action Recognition
We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions.
Learning long-term dependencies for action recognition with a biologically-inspired deep network
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task.
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN
Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.
