On using focal loss for neural network calibration
Miscalibration – a mismatch between a model’s confidence and its correctness – of Deep Neural Networks (DNNs) makes their predictions hard to rely on. Ideally, we want networks to be accurate and calibrated. In this work, we study focal loss as an alternative to the conventional crossentropy loss an...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
2020
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