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trans_learn:2021projects

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2021 Project Page

Transfer learning for battery parameter estimation

Meeting Notes

2021/10/6
  • Attendee: Hanbing, Yanru, Yang
  • Meeting summary:
    • Yanru: on reading: Unsupervised Domain Adaptation for Semantic Segmentation via CBST
    • Hanbing
      • research: optimal transport and domain adaptation problem
      • reading: Joint distribution optimal transportation for domain adaptation; Few-Shot Cross Domain Battery Capacity Estimation
      • slides: optimal transport and domain adaptation
  • TODO:
    • Yanru: make a plan of future work; deeper understanding of papers/ better presentation of work
    • Hanbing: do some research on the application of adversarial network to domain adaptation problem
2021/10/13
  • Attendee: Hanbing, Yanru, Yang
  • Meeting summary:
    • Yanru:
      • reading: transferable semi-supervised semantic segmentation; Adversarial Examples for Semantic Segmentation and Object Detection
      • experiment: tried MIoU metric for H-score evaluation; on coding new dataset
    • Hanbing:
  • TODO:
    • Yanru:
      • more relative reading on transfer learning & U-net (also maybe some survey on semantic segmentation & related area);
      • experiment redesign
        • target and source setting (H-score more suitable for task transfer)
        • dataset with fewer noise & lower difficulty
        • new calculate method for H-score (devision depending on label instead of pixel location)
    • Hanbing:
      • deeper reading ADDA to find its advantage over other adversarial domain adaptation works
      • modify the ADDA model to deal with battery features
2021/10/20
  • Attendee: Hanbing, Yanru, Yang
  • Meeting summary:
    • Yanru:
      • experiment: H-score trial on task transfer using ADE20K – especially good results for 'sky' task
    • Hanbing:
  • TODO:
    • Yanru:
      • recode the experiment with pytorch
      • cityscape dataset preprocessing
      • change of target data size
      • reading
    • Hanbing:
      • change convolution layer to fully connect or conv1D
      • transform battery data into frequency domain
2021/11/10
  • Attendee: Hanbing, Yang
  • Meeting summary:
    • Hanbing:
      • experiment: unsupervised fully connection ADDA using battery pulse data
  • TODO:
    • Hanbing:
      • experiment:
        1. source only: divide source dataset into training set and testing set to see if model is overfitting
        2. target only: using only target data to get target baseline model
        3. transfer learning: finetuning target feature extractor and source classifier useing a few target label
2021/11/14
  • Attendee: Yanru, Yang
  • Meeting summary:
    1. A brief review of several papers related to spatial-temporal data analyses (project_reading.pdf)
    2. Notes: notes1114.png
  • TODO: write background part of project plan
2021/11/17
  • Attendee: Hanbing, Yang
  • Meeting summary:
    • Hanbing:
      • experiment:
        1. source only and target only on original net and simplified net
        2. add penalty term on target model
  • TODO:
    • Hanbing:
      • experiment:
        1. finetunning target model useing a few target label
        2. change visual form of result
trans_learn/2021projects.txt · Last modified: 2022/01/05 21:59 by yang