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Lam Tran

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3 papers

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AAAI Conference 2025 Conference Paper

CASUAL: Conditional Support Alignment for Domain Adaptation with Label Shift

Anh T Nguyen
Lam Tran
Anh Tong
Tuan-Duy H. Nguyen
Toan Tran

Unsupervised domain adaptation (UDA) refers to a domain adaptation framework in which a learning model is trained based on the labeled samples on the source domain and unlabelled ones in the target domain. The dominant existing methods in the field that rely on the classical covariate shift assumption to learn domain-invariant feature representation have yielded suboptimal performance under label distribution shift. In this paper, we propose a novel Conditional Adversarial SUpport ALignment (CASUAL) whose aim is to minimize the conditional symmetric support divergence between the source’s and target domain’s feature representation distributions, aiming at a more discriminative representation for the classification task. We also introduce a novel theoretical target risk bound, which justifies the merits of aligning the supports of conditional feature distributions compared to the existing marginal support alignment approach in the UDA settings. We then provide a complete training process for learning in which the objective optimization functions are precisely based on the proposed target risk bound. Our empirical results demonstrate that CASUAL outperforms other state-of-the-art methods on different UDA benchmark tasks under different label shift conditions.

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NeurIPS Conference 2024 Conference Paper

Enhancing Domain Adaptation through Prompt Gradient Alignment

Hoang Phan
Lam Tran
Quyen Tran
Trung Le

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-language models to learn both domain-invariant and specific features through a set of domain-agnostic and domain-specific learnable prompts. Those studies typically enforce invariant constraints on representation, output, or prompt space to learn such prompts. Differently, we cast UDA as a multiple-objective optimization problem in which each objective is represented by a domain loss. Under this new framework, we propose aligning per-objective gradients to foster consensus between them. Additionally, to prevent potential overfitting when fine-tuning this deep learning architecture, we penalize the norm of these gradients. To achieve these goals, we devise a practical gradient update procedure that can work under both single-source and multi-source UDA. Empirically, our method consistently surpasses other vision language model adaptation methods by a large margin on a wide range of benchmarks. The implementation is available at https: //github. com/VietHoang1512/PGA.

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AAAI Conference 2016 Conference Paper

Privacy-CNH: A Framework to Detect Photo Privacy with Convolutional Neural Network using Hierarchical Features

Lam Tran
Deguang Kong
Hongxia Jin
Ji Liu

Photo privacy is a very important problem in the digital age where photos are commonly shared on social networking sites and mobile devices. The main challenge in photo privacy detection is how to generate discriminant features to accurately detect privacy at risk photos. Existing photo privacy detection works, which rely on low-level vision features, are non-informative to the users regarding what privacy information is leaked from their photos. In this paper, we propose a new framework called Privacy- CNH that utilizes hierarchical features which include both object and convolutional features in a deep learning model to detect privacy at risk photos. The generation of object features enables our model to better inform the users about the reason why a photo has privacy risk. The combination of convolutional and object features provide a richer model to understand photo privacy from different aspects, thus improving photo privacy detection accuracy. Experimental results demonstrate that the proposed model outperforms the state-of-the-art work and the standard convolutional neural network (CNN) with convolutional features on photo privacy detection tasks.

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