94 citations · 126 across the 11 of their papers we have counts for
11 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…
Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
Xingjian Li, Di Hu, Xuhong Li +5
Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain b…
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…
Towards Making Deep Transfer Learning Never Hurt
Ruosi Wan, Haoyi Xiong, Xingjian Li +2
Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for…
Improving Adversarial Robustness via Attention and Adversarial Logit Pairing
Dou Goodman, Xingjian Li, Ji Liu +2
Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversa…
An Empirical Study on Regularization of Deep Neural Networks by Local Rademacher Complexity
Yingzhen Yang, Jiahui Yu, Xingjian Li +2
Regularization of Deep Neural Networks (DNNs) for the sake of improving their generalization capability is important and challenging. The development in this line benefits theoreti…
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional Networks
Xingjian Li, Haoyi Xiong, Hanchao Wang +4
Transfer learning through fine-tuning a pre-trained neural network with an extremely large dataset, such as ImageNet, can significantly accelerate training while the accuracy is fr…