2 citations · 5 across the 20 of their papers we have counts for
10 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…
Enhancing Dataset Distillation via Non-Critical Region Refinement
Minh-Tuan Tran, Trung Le, Xuan-May Le +2
Dataset distillation has become a popular method for compressing large datasets into smaller, more efficient representations while preserving critical information for model trainin…
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
Arpit Garg, Cuong Nguyen, Rafael Felix +3
Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets oft…
Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging
Milad Masroor, Tahir Hassan, Yu Tian +4
Deep learning has achieved remarkable success in image classification and segmentation tasks. However, fairness concerns persist, as models often exhibit biases that disproportiona…
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…
Learning to Complement and to Defer to Multiple Users
Zheng Zhang, Wenjie Ai, Kevin Wells +3
With the development of Human-AI Collaboration in Classification (HAI-CC), integrating users and AI predictions becomes challenging due to the complex decision-making process. This…
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…
DiffAugment: Diffusion based Long-Tailed Visual Relationship Recognition
Parul Gupta, Tuan Nguyen, Abhinav Dhall +3
The task of Visual Relationship Recognition (VRR) aims to identify relationships between two interacting objects in an image and is particularly challenging due to the widely-sprea…
Learning to Complement with Multiple Humans
Zheng Zhang, Cuong Nguyen, Kevin Wells +2
Real-world image classification tasks tend to be complex, where expert labellers are sometimes unsure about the classes present in the images, leading to the issue of learning with…