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Found 7 papers, 4 papers with code
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings.
Meta-learning Control Variates: Variance Reduction with Limited Data
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples is small.
Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel
The security constraints of this method is constructed only with the input and output signal samples of the legal and eavesdropper channels and benefit that training the encoder is completely independent of the decoder.
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Motivated by this, we propose a so-called \textit{Bi-Similarity Network} (\textit{BSNet}) that consists of a single embedding module and a bi-similarity module of two similarity measures.
A Concise Review of Recent Few-shot Meta-learning Methods
Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge.
Coverage Analysis for 3D Terahertz Communication Systems with Blockage and Directional Antennas
We further show that the coverage performance improvement brought by increasing the antenna directivity at APs is higher than that brought by increasing the antenna directivity at UEs.
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
