9 citations · 9 across the 6 of their papers we have counts for
8 papers
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration
Feng Nie, Meixi Chen, Zhirui Zhang +1
Pre-trained language models (PLMs) have exhibited remarkable few-shot learning capabilities when provided a few examples in a natural language prompt as demonstrations of test inst…
Channel Self-Supervision for Online Knowledge Distillation
Shixiao Fan, Xuan Cheng, Xiaomin Wang +5
Recently, researchers have shown an increased interest in the online knowledge distillation. Adopting an one-stage and end-to-end training fashion, online knowledge distillation us…
Feature Mining: A Novel Training Strategy for Convolutional Neural Network
Tianshu Xie, Xuan Cheng, Xiaomin Wang +3
In this paper, we propose a novel training strategy for convolutional neural network(CNN) named Feature Mining, that aims to strengthen the network's learning of the local feature.…
Go Small and Similar: A Simple Output Decay Brings Better Performance
Xuan Cheng, Tianshu Xie, Xiaomin Wang +3
Regularization and data augmentation methods have been widely used and become increasingly indispensable in deep learning training. Researchers who devote themselves to this have c…
Self-supervision of Feature Transformation for Further Improving Supervised Learning
Zilin Ding, Yuhang Yang, Xuan Cheng +2
Self-supervised learning, which benefits from automatically constructing labels through pre-designed pretext task, has recently been applied for strengthen supervised learning. Sin…
Self-supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning
Yuhang Yang, Zilin Ding, Xuan Cheng +2
Traditional self-supervised learning requires CNNs using external pretext tasks (i.e., image- or video-based tasks) to encode high-level semantic visual representations. In this pa…