Tung Lam Tran — accepted AI-conference papers

2025

Boosting Multiple Views for pretrained-based Continual Learning

Quyen Tran, Tung Lam Tran, Khanh Doan, Toan Tran, Dinh Phung, Khoat Than +1

ICLR 2025poster

Recent research has shown that Random Projection (RP) can effectively improve the performance of pre-trained models in Continual learning (CL). The authors hypothesized that using RP to map features onto a higher-dimensional space can make them more linearly separable. In this work, we theoretically…

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2024

Enhancing Domain Adaptation through Prompt Gradient Alignment

Hoang Phan, Tung Lam Tran, Quyen Tran, Trung Le

NeurIPS 2024poster

Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative features. To tackle this, a line of works based on prompt learning leverages the power of large-scale pre-trained vision-la…

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