Rokieplayer / SWE3028

Capstone Design Project (Fall 2021)

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SWE3028

Capstone Design Project (Fall 2021)

Team Building

  • Team A [jjangdol]: YOON SEONGBIN (윤성빈), NAM DEUKYUN (남득윤), WEE SUNGEUN (위성은), and LEE DASOL (이다솔)
  • Team B [bTeam]: Jisu Kim (김지수), Jinhwan Kim (김진환), Seyeon Park (박세연), and Mujin Gwak (곽무진)
  • Team C [coturnix]: Seonghyun Ban (반성현), Dongyoung Choi(최동영), and Minseung Lee (이민승)
  • Team D [EDITH]: Oinar Chingis (칭기즈), Kim Eunmin (김은민), Park Soohun (박수헌), and Gong He (공허)
  • Team E [Exponential]: Pavlov Borislav Georgiev, KIM MINJAE (김민재), KIM YOUNGOH (김영오), and PARK GUERYANG (박거량)
  • Team F [Fancy]: CHA MINJI (차민지), LEE EUNJI (이은지), JO DAEYEOL (조대열), and KIM DAEHEE (김대희)
  • Team G [cookie&cream]: NAMKOONG BOMIN (남궁보민), KIM HANGYU (김한규), SUH JUWON (서주원), CHO GYEONGHYEON (조경현), and CHOI JAEHYUK (최재혁)
  • Team H [The outsiders]: CHE SEUNGYUN (채승윤), UHM JIYONG (엄지용), LEE JISEOP (이지섭), JEONG CHAEWON (정채원), and HONG SEONGJUN (홍성준)

Team Proposals

Common considerations for AI-based projects

  • Supervised or unsupervised?
  • Dataset you want to utilize for training?
    • Manual dataset preparation for labeling?
    • Number of dataset
    • Number of labels (supervised)
    • Ratio (training/validation/test)
  • Goal (e.g., classifier)
    • Clarify the objective of the model
    • Narrow down your scope if too broad -> constraints
    • Set up the objective function (min/max)
  • Algorithm or model
    • Reasoning that you choose that model?
    • CNN-based, RNN-based?, GAN, ensembles?
    • Difference between your model and prior work
    • Applying the existing model (as it is) might not work for your goal
  • Input and output form
    • Input form to be fed into a model
    • e.g., words must be converted into vectors (like word2vec) in NLP
    • pre-processing of raw inputs
    • output: classified category (e.g., softmax) or others?
  • Defining unique challenges for your own problem
  • Existing techniques to take advantage of
    • Open sources
    • e.g., OCR (optical character recognition) for recognizing chars
  • Limitations (every work has limitations)
    • Constraints
    • Out of scope
  • Evaluation
    • Evaluation metrics? F1 (with FPR/FNR), accuracy or AUC? (supervised)
    • Assessment on GAN outputs?
    • Comparison with another model at least one comparison: performance comparison with previous approaches
      • Availability of comparing models (open source?)
  • Computation resource (e.g., GPU)
    • Out of scope; you should find your own
    • Cloud GPU?

About

Capstone Design Project (Fall 2021)