Triplet loss to encourages dissimilar alignments be distant from similar alignments
Siamese network and triplet loss can be combined with different input types. For example; as in the previous slide, the rotation change is more understandable in sliced MIP data. Thus, feature extraction phase can be more successful.
How we built it?
Step by step:
Data Analysis
Data Load
Design
Training
Validation
Repeat – 3
Challenges we ran into
Generalization on the unseen radar sensors
Understanding data in short notice
Accomplishments that we're proud of
Ideas that we bring into table
Teamwork
Free swag that we collected
What we learned
It is our first experience with the Microwave Imaging dataset
We get to know new companies and people
What's next for
Improving the Augmentation methods
Working on different approaches for generalize the network better to get accurate alignment
results on the unseen radar sensors
Attention can be used for extracting relations better. Attention is all you need
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