林佳縈 Chia Ying Lin

Computer Vision Reseacher

  Computer Vision Lab, NTHU


With 4-year solid training in computer science fundamentals, 2-year independent research experience in computer vision, and diverse hands-on development experiences, I always pursue excellence, crave new challenges, and am open to all possibilities.  

  [email protected]

https://github.com/lykasbongbongbong

linkedin.com/in/lykas-chia-ying-lin

  0937-464-176

Skills

Languages


  • Python (familiar)
  • C++, C
  • JAVA

Deep Learning Frameworks


  • PyTorch (familiar)
  • Tensorflow

CV & DL Libraries


  • OpenCV
  • Keras

Development Tools


  • Git/Github
  • RESTFul APIs

Web Development


  • HTML, CSS, JS
  • Python Flask
  • PHP, Laravel
  • MySQL, NoSQL(mongoDB)
  • AWS

Others


  • English TOEFL iBT 97
  • English TOEIC 935 
  • German B2

Education


Master's Studies, Computer Vision Lab, NTHU

Dept. Information Systems and Application, Sep 2020~Now

  • Anomaly detection and segmentation for smart manufacturing as primary research target

Bachelor Degree, WASN Lab, NCU

Dept. Computer Science, Sep 2016 ~ June 2020

Exchange Student, Hochschule München, Germany

Dept. Informatik (Computer Science), Sep 2019 ~ Feb 2020

Experiences

August 2021

Attendee,

Machine Learning Summer School, NTU

August 2021

Backend Developer (Anomaly Detection Demo Website),

CVLab, FUTEX 2021

  • Use Python Flask, MySQL for RestFul APIs development
  • Optimized backend system with Python threading to support sudden massive traffic

June 2018 ~ July 2019, 1y1m

Full-Stack Web Developer (NCU Internship Web)

Career Center NCU

  • Reconstruct website with Laravel Framework
  • Optimize backend and database to handle larger amounts of access
  • Over 30% of users received intern opportunities via this site 
  • Website Link: https://ncuinternship.careercenter.ncu.edu.tw/

Master Thesis: 

SABDN: Self-Attention Based Deviation Network for few-shot anomaly detection and segmentation (Under Review ECCV 2022)



Contributions

  • Combine self-attention mechanism with feature extraction CNN network and anomaly synthesis mechanism for anomaly scoring to achieve outstanding anomaly detection accuracy under few-shot setting
  • Reach SOTA performance on benchmark dataset MVTecAD dataset and BTAD dataset with over 90 percent reduction in training data requirements

Side Projects




GlueGAN: a generative model based on SuperGlue structure

  • Extend MagicLeap’s SuperGlue end-to-end GNN concept for object synthesis to solve synthetic object image generation problem
  • 10% accuracy improvement compared to initial GAN backbone 

Let's play GAN with flows and friends!

  • Generate synthetic object images with multi-label conditions with conditional GAN manner
  • Human faces generation by conditional normalizing flow

2048: by Temporal Difference Learning Approach (RL)

  • Construct TD-learning algorithm and design own n-tuple network to solve 2048 game
  • Reach 98% 2048-tile win rate in 1000 games

The LunarLander: under Deep Q-Network and DDPG manner (RL)

  • Implement DQN and DDPG network to solve LunarLander game problem
  • Thoroughly understand Deep Q-learning, actor-critic mechanism

Real-Time car detection and counting system

  • Retrain YOLOv3 with our own hatchback dataset
  • Reach average accuracy: 99% on both day-light and evening scenarios
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