2 citations · 10 across the 25 of their papers we have counts for
7 papers · 1 filter
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…
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.…
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…
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…
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…
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…