Publications (11)
Quick Bypass Mechanism of Zero-Shot Diffusion-Based Image Restoration
Yu-Shan Tai, An-Yeu, Wu
Recent advancements in diffusion models have demonstrated remarkable success in various image generation tasks. Building upon these achievements, diffusion models have also been ef…
Efficient Coarse-to-Fine Diffusion Models with Time Step Sequence Redistribution
Yu-Shan Tai, An-Yeu, Wu
Recently, diffusion models (DMs) have made significant strides in high-quality image generation. However, the multi-step denoising process often results in considerable computation…
Post-Training Quantization for Vision Mamba with k-Scaled Quantization and Reparameterization
Bo-Yun Shi, Yi-Cheng Lo, An-Yeu +2
The Mamba model, utilizing a structured state-space model (SSM), offers linear time complexity and demonstrates significant potential. Vision Mamba (ViM) extends this framework to…
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer
Yu-Shan Tai, Ming-Guang Lin, An-Yeu +1
Vision transformers (ViTs) have achieved remarkable performance in various computer vision tasks. However, intensive memory and computation requirements impede ViTs from running on…
AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification
Yi-Ta Chen, Yu-Chuan Chuang, An-Yeu +1
In this paper, we propose an AdaBoost-assisted extreme learning machine for efficient online sequential classification (AOS-ELM). In order to achieve better accuracy in online sequ…
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning
Hao-Wei Chiang, Chi-Tse Huang, Hsiang-Yun Cheng +4
While memory-augmented neural networks (MANNs) offer an effective solution for few-shot learning (FSL) by integrating deep neural networks with external memory, the capacity requir…
C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning
Cheng-Yen Hsieh, Yu-Chuan Chuang, An-Yeu +1
Most existing studies improve the efficiency of Split learning (SL) by compressing the transmitted features. However, most works focus on dimension-wise compression that transforms…
LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer
Jiing-Ping Wang, Ming-Guang Lin, An-Yeu +1
With the rise of Transformer models in NLP and CV domain, Multi-Head Attention has been proven to be a game-changer. However, its expensive computation poses challenges to the mode…
Relay-Assisted Carrier Aggregation (RACA) Uplink System for Enhancing Data Rate of Extended Reality (XR)
Chi-Wei Chen, Wen-Chiao Tsai, Lung-Sheng Tsai +2
In Extended Reality (XR) applications, high data rates and low latency are crucial for immersive experiences. Uplink transmission in XR is challenging due to the limited antennas a…
MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer
Yu-Shan Tai, An-Yeu, Wu
While vision transformers (ViTs) have shown great potential in computer vision tasks, their intense computation and memory requirements pose challenges for practical applications.…
MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor
Win-Ken Beh, Yi-Hsuan Wu, An-Yeu +1
This paper describes an open-access database focusing on the study of mental workload (MW) assessment system for wearable devices. A wristband photoplethysmogram (PPG) was provided…