Search Results for author:
Found 8 papers, 2 papers with code
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception
Driver distraction has become a significant cause of severe traffic accidents over the past decade.
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images
Inspired by the recent advance in semi-supervised learning and deep learning, we propose mfTacoma to learn alternative deep representations in the context of TMA image scoring.
Domain Adaptation with Incomplete Target Domains
In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning
The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.
Mutual Learning Network for Multi-Source Domain Adaptation
However, in practice the labeled data can come from multiple source domains with different distributions.
AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN
Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e. g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data.
K-nearest Neighbor Search by Random Projection Forests
Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.
