4,423 research outputs found
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
Training fingerprint recognition models using synthetic data has recently
gained increased attention in the biometric community as it alleviates the
dependency on sensitive personal data. Existing approaches for fingerprint
generation are limited in their ability to generate diverse impressions of the
same finger, a key property for providing effective data for training
recognition models. To address this gap, we present FPGAN-Control, an identity
preserving image generation framework which enables control over the
fingerprint's image appearance (e.g., fingerprint type, acquisition device,
pressure level) of generated fingerprints. We introduce a novel appearance loss
that encourages disentanglement between the fingerprint's identity and
appearance properties. In our experiments, we used the publicly available NIST
SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the
merits of FPGAN-Control, both quantitatively and qualitatively, in terms of
identity preservation level, degree of appearance control, and low
synthetic-to-real domain gap. Finally, training recognition models using only
synthetic datasets generated by FPGAN-Control lead to recognition accuracies
that are on par or even surpass models trained using real data. To the best of
our knowledge, this is the first work to demonstrate this
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Uncertainty quantification (UQ) is an important component of molecular
property prediction, particularly for drug discovery applications where model
predictions direct experimental design and where unanticipated imprecision
wastes valuable time and resources. The need for UQ is especially acute for
neural models, which are becoming increasingly standard yet are challenging to
interpret. While several approaches to UQ have been proposed in the literature,
there is no clear consensus on the comparative performance of these models. In
this paper, we study this question in the context of regression tasks. We
systematically evaluate several methods on five benchmark datasets using
multiple complementary performance metrics. Our experiments show that none of
the methods we tested is unequivocally superior to all others, and none
produces a particularly reliable ranking of errors across multiple datasets.
While we believe these results show that existing UQ methods are not sufficient
for all common use-cases and demonstrate the benefits of further research, we
conclude with a practical recommendation as to which existing techniques seem
to perform well relative to others
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