Projects

Taiwan-LLM Tutor: Revolutionizing Taiwanese Secondary Education with Large Language Model

Jia-Wei Liao, Ji-Jia Wu, Kun-Hsiang Lin, and Kang-Yang Huang, Applied Deep Learning Final Project, 2023.

Multimodal Pathological Voice Classification

Chun-Hsien Chen, Shu-Cheng Zheng, Jia-Wei Liao, and Yi-Cheng Hung, AICUP, 2023

Optimizing Marketing Strategies and Data Analysis for Bag Brand

Yi-Jhen Ciou, I-Ting Wang, Kai Yu, and Jia-Wei Liao, TMBA Final Project, 2023

A Small Object Detection Framework for Unmanned Aerial Vehicles Images

Jing-En Huang and Jia-Wei Liao, AI CUP, 2022.

We developed a state-of-the-art one-stage detection model as the baseline framework for the competition, with the goal of accurately detecting small objects. As a result, we achieved a top 5 ranking in the competition with a public score of 0.7394 and a private score of 0.7550.

Crop Image Recognition

Jia-Wei Liao, Yen-Jia Chen, Yi-Cheng Hung, Jing-En Hung, and Shang-Yen Lee, AI CUP, 2022.

We explored various models, applied auto-augmentation techniques to diversify the dataset, and trained with CNN base models like EfficientNet and Transformer-base models like Swin. Our results showed that using SGD as the optimizer had better convergence than AdamW and higher image resolution led to better training performance. We found that the Swin model performed better on lower resolution data while CNN base models performed better on high resolution data. To evaluate the stability and advantage of ensemble combination, we applied TTA and ensemble to both public and private datasets. As a result, we achieved a public ranking of 9th and a private ranking of 8th, with scores of 0.9329 and 0.9344 respectively.

Explainable Information Tagging for Natural Language Understanding

Jia-Wei Liao, Jung-Mei Chu, and Chia-Chi Huang, AI CUP, 2022.

We constructed an explainable deep learning model for a NLP task by converting it into a summary task. We applied the T5 model to two datasets with different pre-processing methods and surpassed the original data's baseline. We achieved a public score of 0.801772 and a private score of 0.85190.

Contour Segmentation for Spread Through Air Spaces in Lung Adenocarcinoma

Jia-Wei Liao, Kuok-Tong Ng, and Yi-Cheng Hung, AI CUP, 2022.

We developed a UNet-based model with a diverse backbone, addressing the imbalance of foreground and background through a combination of DiceLoss and Focal Loss, and enhancing robustness through auto-augmentation. Our post-processing strategies improved accuracy, resulting in a 3rd place public ranking and 16th place private ranking, with scores of 0.9194 and 0.9109 respectively.

Supervised Learning for Few-Shot Orchid types Classification with Prior Guided Feature

Yu-Hsi Chen, Jia-Wei Liao, and Kuok-Tong Ng, AI CUP, 2022.

We developed a few-shot learning approach to accurately distinguish visually similar orchid species, addressing challenges posed by biotechnology-created species and limited training data. Using various models, data augmentations, and optimization techniques, we ensembled top predictions, achieving a Macro-F1 score of 0.9115 (public) and 0.8096 (private), ranking 15th out of 743 teams.

MediaTek Low-power Segmentation Competition

Jia-Wei Liao, Machine Learning Final Project, 2022.

We propose a lightweight deep learning-based semantic segmentation model that is suitable for constrained embedded systems and is optimized for traffic scene analysis in Asian countries, such as Taiwan. The model focuses on improving segmentation accuracy, reducing power consumption, and achieving real-time performance. The model is also designed to be deployed on MediaTek's Dimensity Series platform. The evaluation metric used is mean Intersection over Union (mIoU) which is a widely used metric for multi-class semantic segmentation tasks. Our experiments yielded a test public score of 0.5624.

Ultrasound Nerve Segmentation

Jia-Wei Liao, Kuok-Tong Ng, and Yi-Cheng Hung, VRDL Final Project (Kaggle), 2021

We present a deep learning-based approach for ultrasound nerve segmentation using the UNet architecture with the EfficientNet backbone. Two novel methods, Erosion Mask Smoothing (ELS) and adaptive Single Model Ensemble (ASME) were proposed to improve the segmentation performance. The evaluation metric used is the Dice Similarity Coefficient (DSC), and the results show that the proposed approach outperforms the baseline with a test private score of 0.7234, achieved through the use of ASME.

Low Rank Matrix Factorization for Recommender System

Jia-Wei Liao, Kuok-Tong Ng, and Yi-Cheng Hung, Introduction to Scientific Computing Final Project, 2021

Sentiment Analysis of Food Reviews on Yelp Platform

Jia-Wei Liao, Yi-Cheng Hung, and Yu-Lin Tsai, Introduction to Data Science Final Project, 2021

Solving the Biharmonic Equation by Deep Neural Network

Jia-Wei Liao, Yu-Hsi Chen, and Woan-Rong Huang, Numerical Partial Differential Equations Final Project, 2021

3D Shape Morphing Animation based on Poisson Image Editing

Jia-Wei Liao and Kuok-Tong Ng, 3D Computationl Geometry Final Project, 2021

Mean Curvature Flow on Graphs for Image Denoising

Jia-Wei Liao and Kuok-Tong Ng, Image Processing with Partial Differential Equations Final Project, 2020

Online Dictionary Learning for Image Inpainting

Jia-Wei Liao and Kuok-Tong Ng, Optimization for Data Science Final Project, 2020

Variational Models and Numerical Methods for Image Processing

Jia-Wei Liao, Chun-Hsien Chen, and Chen-Yang Dai, NCTS-USRP Final Project, 2020