118 citations · 219 across the 5 of their papers we have counts for
5 papers
Exploiting Domain-Specific Features to Enhance Domain Generalization
Manh-Ha Bui, Toan Tran, Anh Tuan Tran +1
Domain Generalization (DG) aims to train a model, from multiple observed source domains, in order to perform well on unseen target domains. To obtain the generalization capability,…
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Anh Tong, Toan Tran, Hung Bui +1
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitne…
Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid +1
Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources…
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning
Thanh-Toan Do, Toan Tran, Ian Reid +3
We propose a method that substantially improves the efficiency of deep distance metric learning based on the optimization of the triplet loss function. One epoch of such training p…
A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro +2
Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…