22 citations · 174 across the 40 of their papers we have counts for
4 papers · 1 filter
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…
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…