11 citations · 21 across the 5 of their papers we have counts for
9 papers
Adaptive Consistency Regularization for Semi-Supervised Transfer Learning
Abulikemu Abuduweili, Xingjian Li, Humphrey Shi +2
While recent studies on semi-supervised learning have shown remarkable progress in leveraging both labeled and unlabeled data, most of them presume a basic setting of the model is…
Temporal Relational Modeling with Self-Supervision for Action Segmentation
Dong Wang, Di Hu, Xingjian Li +1
Temporal relational modeling in video is essential for human action understanding, such as action recognition and action segmentation. Although Graph Convolution Networks (GCNs) ha…
Measuring Information Transfer in Neural Networks
Xiao Zhang, Xingjian Li, Dejing Dou +1
Quantifying the information content in a neural network model is essentially estimating the model's Kolmogorov complexity. Recent success of prequential coding on neural networks p…
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup
Xingjian Li, Haoyi Xiong, Haozhe An +2
Transferring knowledge from large source datasets is an effective way to fine-tune the deep neural networks of the target task with a small sample size. A great number of algorithm…
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
Xingjian Li, Haoyi Xiong, Haozhe An +2
Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. While the accuracy could be…
COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization
Xingjian Li, Haoyi Xiong, Haozhe An +2
Softening labels of training datasets with respect to data representations has been frequently used to improve the training of deep neural networks (DNNs). While such a practice ha…