2 citations · 10 across the 25 of their papers we have counts for
25 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Ordering-based Causal Discovery via Generalized Score Matching
Vy Vo, He Zhao, Trung Le +2
Learning DAG structures from purely observational data remains a long-standing challenge across scientific domains. An emerging line of research leverages the score of the data dis…
Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation
Tung-Long Vuong, Hoang Phan, Vy Vo +4
Recent approaches leveraging multi-modal pre-trained models like CLIP for Unsupervised Domain Adaptation (UDA) have shown significant promise in bridging domain gaps and improving…
Promoting Ensemble Diversity with Interactive Bayesian Distributional Robustness for Fine-tuning Foundation Models
Ngoc-Quan Pham, Tuan Truong, Quyen Tran +3
We introduce Interactive Bayesian Distributional Robustness (IBDR), a novel Bayesian inference framework that allows modeling the interactions between particles, thereby enhancing…
Why Domain Generalization Fail? A View of Necessity and Sufficiency
Long-Tung Vuong, Vy Vo, Hien Dang +5
Despite a strong theoretical foundation, empirical experiments reveal that existing domain generalization (DG) algorithms often fail to consistently outperform the ERM baseline. We…
Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization
Haocheng Luo, Tuan Truong, Tung Pham +3
Sharpness-Aware Minimization (SAM) has attracted significant attention for its effectiveness in improving generalization across various tasks. However, its underlying principles re…
Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them
Anh Bui, Trang Vu, Long Vuong +5
Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The c…
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation
Minh-Tuan Tran, Trung Le, Xuan-May Le +3
Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data.…
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation
Anh Bui, Long Vuong, Khanh Doan +4
Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A pr…
Connective Viewpoints of Signal-to-Noise Diffusion Models
Khanh Doan, Long Tung Vuong, Tuan Nguyen +5
Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpola…
MetaAug: Meta-Data Augmentation for Post-Training Quantization
Cuong Pham, Hoang Anh Dung, Cuong C. Nguyen +4
Post-Training Quantization (PTQ) has received significant attention because it requires only a small set of calibration data to quantize a full-precision model, which is more pract…
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning
Cuong Pham, Cuong C. Nguyen, Trung Le +3
Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared…
Enhancing Domain Adaptation through Prompt Gradient Alignment
Hoang Phan, Lam Tran, Quyen Tran +1
Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative fea…
Agnostic Sharpness-Aware Minimization
Van-Anh Nguyen, Quyen Tran, Tuan Truong +3
Sharpness-aware minimization (SAM) has been instrumental in improving deep neural network training by minimizing both the training loss and the sharpness of the loss landscape, lea…
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
Minh-Tuan Tran, Trung Le, Xuan-May Le +2
Federated Class-Incremental Learning (FCIL) is an underexplored yet pivotal issue, involving the dynamic addition of new classes in the context of federated learning. In this field…
Diversity-Aware Agnostic Ensemble of Sharpness Minimizers
Anh Bui, Vy Vo, Tung Pham +2
There has long been plenty of theoretical and empirical evidence supporting the success of ensemble learning. Deep ensembles in particular take advantage of training randomness and…
Frequency Attention for Knowledge Distillation
Cuong Pham, Van-Anh Nguyen, Trung Le +3
Knowledge distillation is an attractive approach for learning compact deep neural networks, which learns a lightweight student model by distilling knowledge from a complex teacher…
Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts
Anh Bui, Khanh Doan, Trung Le +3
Diffusion models have demonstrated remarkable capability in generating high-quality visual content from textual descriptions. However, since these models are trained on large-scale…
Optimal Transport for Structure Learning Under Missing Data
Vy Vo, He Zhao, Trung Le +2
Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considerin…
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning
Khanh Doan, Quyen Tran, Tung Lam Tran +3
Mitigating catastrophic forgetting is a key hurdle in continual learning. Deep Generative Replay (GR) provides techniques focused on generating samples from prior tasks to enhance…
KOPPA: Improving Prompt-based Continual Learning with Key-Query Orthogonal Projection and Prototype-based One-Versus-All
Quyen Tran, Hoang Phan, Lam Tran +4
Drawing inspiration from prompt tuning techniques applied to Large Language Models, recent methods based on pre-trained ViT networks have achieved remarkable results in the field o…
Generalization Bounds for Robust Contrastive Learning: From Theory to Practice
Ngoc N. Tran, Lam Tran, Hoang Phan +5
Contrastive Learning first extracts features from unlabeled data, followed by linear probing with labeled data. Adversarial Contrastive Learning (ACL) integrates Adversarial Traini…
Cross-adversarial local distribution regularization for semi-supervised medical image segmentation
Thanh Nguyen-Duc, Trung Le, Roland Bammer +3
Medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data. Existing semi-supervised…
NAYER: Noisy Layer Data Generation for Efficient and Effective Data-free Knowledge Distillation
Minh-Tuan Tran, Trung Le, Xuan-May Le +3
Data-Free Knowledge Distillation (DFKD) has made significant recent strides by transferring knowledge from a teacher neural network to a student neural network without accessing th…