55 citations · 75 across the 8 of their papers we have counts for
9 papers
Universal Multi-Domain Translation via Diffusion Routers
Duc Kieu, Kien Do, Tuan Hoang +4
Multi-domain translation (MDT) aims to learn translations between multiple domains, yet existing approaches either require fully aligned tuples or can only handle domain pairs seen…
Bidirectional Diffusion Bridge Models
Duc Kieu, Kien Do, Toan Nguyen +2
Diffusion bridges have shown potential in paired image-to-image (I2I) translation tasks. However, existing methods are limited by their unidirectional nature, requiring separate mo…
Variational Flow Models: Flowing in Your Style
Kien Do, Duc Kieu, Toan Nguyen +4
We propose a systematic training-free method to transform the probability flow of a "linear" stochastic process characterized by the equation X_{t}=a_{t}X_{0}+σ_{t}X_{1} into a str…
Domain Generalisation via Risk Distribution Matching
Toan Nguyen, Kien Do, Bao Duong +1
We propose a novel approach for domain generalisation (DG) leveraging risk distributions to characterise domains, thereby achieving domain invariance. In our findings, risk distrib…
Causal Inference via Style Transfer for Out-of-distribution Generalisation
Toan Nguyen, Kien Do, Duc Thanh Nguyen +2
Out-of-distribution (OOD) generalisation aims to build a model that can generalise well on an unseen target domain using knowledge from multiple source domains. To this end, the mo…
Diffeomorphic Information Neural Estimation
Bao Duong, Thin Nguyen
Mutual Information (MI) and Conditional Mutual Information (CMI) are multi-purpose tools from information theory that are able to naturally measure the statistical dependencies bet…
Efficient Classification with Counterfactual Reasoning and Active Learning
Azhar Mohammed, Dang Nguyen, Bao Duong +1
Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision. However, applying data augmentation…
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo +3
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and eff…
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Duc Tam Nguyen, Maximilian Dax, Chaithanya Kumar Mummadi +4
Deep neural network (DNN) based salient object detection in images based on high-quality labels is expensive. Alternative unsupervised approaches rely on careful selection of multi…