Inspiration
Most models provide no confidence measure for its predictions due to argmax(). Thats a problem if you want to compare the predictions for objects from different sensors (multimodalilty)
What it does
- we colored the images with a different intensity proportional to the output of the neuron (plotted logits)
- with a pre-build sotA model we predict the class of objects in images
- with this classification we trained a separate convNN to predict errors in the classification.
How we built it
python (tensorflow) with google deeplab
Challenges we ran into
- core of challenge was already popular research area, so we found it hard to identify possible contributions for this scope
- modifying the current state-of-the-art model-graph to include dropout for inference (in order to achieve dropout sampling for confidence estimation) proved a technological challenge
Accomplishments that we're proud of
hacked dropout into model
What we learned
we learned to develop not perfect NN-models in short time.
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