73 citations · 152 across the 24 of their papers we have counts for
25 papers
Explore the Power of Synthetic Data on Few-shot Object Detection
Shaobo Lin, Kun Wang, Xingyu Zeng +1
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance o…
An Effective Crop-Paste Pipeline for Few-shot Object Detection
Shaobo Lin, Kun Wang, Xingyu Zeng +1
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. However, detecting novel categories with only a few…
Explore the Power of Dropout on Few-shot Learning
Shaobo Lin, Xingyu Zeng, Rui Zhao
The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this pa…
A Unified Framework with Meta-dropout for Few-shot Learning
Shaobo Lin, Xingyu Zeng, Rui Zhao
Conventional training of deep neural networks usually requires a substantial amount of data with expensive human annotations. In this paper, we utilize the idea of meta-learning to…
On ADMM in Deep Learning: Convergence and Saturation-Avoidance
Jinshan Zeng, Shao-Bo Lin, Yuan Yao +1
In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \textit{sigmoid-ADM…
Construction of neural networks for realization of localized deep learning
Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou
The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis a…