I need help designing an experiment to study the impact of Pelt/Okumoto (Gaussian) noise and label error on U-Net performance. The focus is on measuring robustness to noise. Dimensions to be tested are label adaption vs header adaption
Requirements:
- Experiment design expertise
- Understanding of U-Net and noise effects (Pelt/ Okumoto)
- Ability to measure and analyze robustness metrics
- Suitable for all passenger vehicles (cars, trucks, motorcycles)
Ideal skills and experience:
- Expertise in automotive electronics
- Experience with wiring and connectivity
- Ability to design a reliable and safe blinking mechanism
so, there is no baseline. the dataset holds assumptions and is later supposed to serve as a conceptual baseline (the target - true satellite images - is more complex). see a description on how the dataset is synthesised below. There are two classes - vegetation vs no vegetation. The resolution is downsampled to 10m*10m per pixel which intriduces uncertainty in the widest sense of the word.
The dataset is 1200 consisting of falsely coloured urban layouts. The target classes are vegetation vs no vegetation but underlying the images hold more subclasses. The Layouts are then subjected to noise in either a Pelt regime or a Okumoto regime. Then additionally images are downsampled to a resolution of 10m*10m resulting in mixed pixels (and introduding uncertainty).
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